Overview

Dataset statistics

Number of variables151
Number of observations42536
Missing cells3840156
Missing cells (%)59.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory49.0 MiB
Average record size in memory1.2 KiB

Variable types

Unsupported83
Numeric29
Categorical35
Boolean4

Warnings

pymnt_plan has constant value "False" Constant
initial_list_status has constant value "False" Constant
out_prncp has constant value "0.0" Constant
out_prncp_inv has constant value "0.0" Constant
collections_12_mths_ex_med has constant value "0.0" Constant
policy_code has constant value "1.0" Constant
application_type has constant value "Individual" Constant
chargeoff_within_12_mths has constant value "0.0" Constant
hardship_flag has constant value "False" Constant
disbursement_method has constant value "Cash" Constant
int_rate has a high cardinality: 394 distinct values High cardinality
emp_title has a high cardinality: 30658 distinct values High cardinality
issue_d has a high cardinality: 55 distinct values High cardinality
url has a high cardinality: 42535 distinct values High cardinality
desc has a high cardinality: 28963 distinct values High cardinality
title has a high cardinality: 21264 distinct values High cardinality
zip_code has a high cardinality: 837 distinct values High cardinality
earliest_cr_line has a high cardinality: 530 distinct values High cardinality
revol_util has a high cardinality: 1119 distinct values High cardinality
last_pymnt_d has a high cardinality: 112 distinct values High cardinality
next_pymnt_d has a high cardinality: 98 distinct values High cardinality
last_credit_pull_d has a high cardinality: 133 distinct values High cardinality
debt_settlement_flag_date has a high cardinality: 63 distinct values High cardinality
settlement_date has a high cardinality: 61 distinct values High cardinality
loan_amnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 2 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 2 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 1 other fieldsHigh correlation
fico_range_low is highly correlated with fico_range_highHigh correlation
fico_range_high is highly correlated with fico_range_lowHigh correlation
total_pymnt is highly correlated with total_pymnt_inv and 1 other fieldsHigh correlation
total_pymnt_inv is highly correlated with funded_amnt_inv and 2 other fieldsHigh correlation
total_rec_prncp is highly correlated with total_pymnt and 1 other fieldsHigh correlation
term is highly correlated with policy_code and 9 other fieldsHigh correlation
policy_code is highly correlated with term and 27 other fieldsHigh correlation
disbursement_method is highly correlated with term and 27 other fieldsHigh correlation
delinq_amnt is highly correlated with policy_code and 13 other fieldsHigh correlation
application_type is highly correlated with term and 27 other fieldsHigh correlation
grade is highly correlated with policy_code and 10 other fieldsHigh correlation
out_prncp is highly correlated with term and 27 other fieldsHigh correlation
loan_status is highly correlated with policy_code and 9 other fieldsHigh correlation
chargeoff_within_12_mths is highly correlated with term and 27 other fieldsHigh correlation
sub_grade is highly correlated with policy_code and 10 other fieldsHigh correlation
purpose is highly correlated with policy_code and 9 other fieldsHigh correlation
acc_now_delinq is highly correlated with policy_code and 13 other fieldsHigh correlation
settlement_date is highly correlated with policy_code and 14 other fieldsHigh correlation
next_pymnt_d is highly correlated with policy_code and 10 other fieldsHigh correlation
collections_12_mths_ex_med is highly correlated with term and 27 other fieldsHigh correlation
verification_status is highly correlated with policy_code and 9 other fieldsHigh correlation
pub_rec_bankruptcies is highly correlated with policy_code and 12 other fieldsHigh correlation
issue_d is highly correlated with policy_code and 9 other fieldsHigh correlation
addr_state is highly correlated with policy_code and 9 other fieldsHigh correlation
debt_settlement_flag_date is highly correlated with policy_code and 13 other fieldsHigh correlation
hardship_flag is highly correlated with term and 27 other fieldsHigh correlation
pymnt_plan is highly correlated with term and 27 other fieldsHigh correlation
settlement_status is highly correlated with policy_code and 13 other fieldsHigh correlation
out_prncp_inv is highly correlated with term and 27 other fieldsHigh correlation
home_ownership is highly correlated with policy_code and 9 other fieldsHigh correlation
initial_list_status is highly correlated with term and 27 other fieldsHigh correlation
tax_liens is highly correlated with policy_code and 13 other fieldsHigh correlation
emp_length is highly correlated with policy_code and 9 other fieldsHigh correlation
debt_settlement_flag is highly correlated with policy_code and 12 other fieldsHigh correlation
member_id has 42536 (100.0%) missing values Missing
emp_title has 2627 (6.2%) missing values Missing
emp_length has 1113 (2.6%) missing values Missing
desc has 13294 (31.3%) missing values Missing
mths_since_last_record has 38885 (91.4%) missing values Missing
next_pymnt_d has 39787 (93.5%) missing values Missing
mths_since_last_major_derog has 42536 (100.0%) missing values Missing
annual_inc_joint has 42536 (100.0%) missing values Missing
dti_joint has 42536 (100.0%) missing values Missing
verification_status_joint has 42536 (100.0%) missing values Missing
tot_coll_amt has 42536 (100.0%) missing values Missing
tot_cur_bal has 42536 (100.0%) missing values Missing
open_acc_6m has 42536 (100.0%) missing values Missing
open_act_il has 42536 (100.0%) missing values Missing
open_il_12m has 42536 (100.0%) missing values Missing
open_il_24m has 42536 (100.0%) missing values Missing
mths_since_rcnt_il has 42536 (100.0%) missing values Missing
total_bal_il has 42536 (100.0%) missing values Missing
il_util has 42536 (100.0%) missing values Missing
open_rv_12m has 42536 (100.0%) missing values Missing
open_rv_24m has 42536 (100.0%) missing values Missing
max_bal_bc has 42536 (100.0%) missing values Missing
all_util has 42536 (100.0%) missing values Missing
total_rev_hi_lim has 42536 (100.0%) missing values Missing
inq_fi has 42536 (100.0%) missing values Missing
total_cu_tl has 42536 (100.0%) missing values Missing
inq_last_12m has 42536 (100.0%) missing values Missing
acc_open_past_24mths has 42536 (100.0%) missing values Missing
avg_cur_bal has 42536 (100.0%) missing values Missing
bc_open_to_buy has 42536 (100.0%) missing values Missing
bc_util has 42536 (100.0%) missing values Missing
mo_sin_old_il_acct has 42536 (100.0%) missing values Missing
mo_sin_old_rev_tl_op has 42536 (100.0%) missing values Missing
mo_sin_rcnt_rev_tl_op has 42536 (100.0%) missing values Missing
mo_sin_rcnt_tl has 42536 (100.0%) missing values Missing
mort_acc has 42536 (100.0%) missing values Missing
mths_since_recent_bc has 42536 (100.0%) missing values Missing
mths_since_recent_bc_dlq has 42536 (100.0%) missing values Missing
mths_since_recent_inq has 42536 (100.0%) missing values Missing
mths_since_recent_revol_delinq has 42536 (100.0%) missing values Missing
num_accts_ever_120_pd has 42536 (100.0%) missing values Missing
num_actv_bc_tl has 42536 (100.0%) missing values Missing
num_actv_rev_tl has 42536 (100.0%) missing values Missing
num_bc_sats has 42536 (100.0%) missing values Missing
num_bc_tl has 42536 (100.0%) missing values Missing
num_il_tl has 42536 (100.0%) missing values Missing
num_op_rev_tl has 42536 (100.0%) missing values Missing
num_rev_accts has 42536 (100.0%) missing values Missing
num_rev_tl_bal_gt_0 has 42536 (100.0%) missing values Missing
num_sats has 42536 (100.0%) missing values Missing
num_tl_120dpd_2m has 42536 (100.0%) missing values Missing
num_tl_30dpd has 42536 (100.0%) missing values Missing
num_tl_90g_dpd_24m has 42536 (100.0%) missing values Missing
num_tl_op_past_12m has 42536 (100.0%) missing values Missing
pct_tl_nvr_dlq has 42536 (100.0%) missing values Missing
percent_bc_gt_75 has 42536 (100.0%) missing values Missing
pub_rec_bankruptcies has 1366 (3.2%) missing values Missing
tot_hi_cred_lim has 42536 (100.0%) missing values Missing
total_bal_ex_mort has 42536 (100.0%) missing values Missing
total_bc_limit has 42536 (100.0%) missing values Missing
total_il_high_credit_limit has 42536 (100.0%) missing values Missing
revol_bal_joint has 42536 (100.0%) missing values Missing
sec_app_fico_range_low has 42536 (100.0%) missing values Missing
sec_app_fico_range_high has 42536 (100.0%) missing values Missing
sec_app_earliest_cr_line has 42536 (100.0%) missing values Missing
sec_app_inq_last_6mths has 42536 (100.0%) missing values Missing
sec_app_mort_acc has 42536 (100.0%) missing values Missing
sec_app_open_acc has 42536 (100.0%) missing values Missing
sec_app_revol_util has 42536 (100.0%) missing values Missing
sec_app_open_act_il has 42536 (100.0%) missing values Missing
sec_app_num_rev_accts has 42536 (100.0%) missing values Missing
sec_app_chargeoff_within_12_mths has 42536 (100.0%) missing values Missing
sec_app_collections_12_mths_ex_med has 42536 (100.0%) missing values Missing
sec_app_mths_since_last_major_derog has 42536 (100.0%) missing values Missing
hardship_type has 42536 (100.0%) missing values Missing
hardship_reason has 42536 (100.0%) missing values Missing
hardship_status has 42536 (100.0%) missing values Missing
deferral_term has 42536 (100.0%) missing values Missing
hardship_amount has 42536 (100.0%) missing values Missing
hardship_start_date has 42536 (100.0%) missing values Missing
hardship_end_date has 42536 (100.0%) missing values Missing
payment_plan_start_date has 42536 (100.0%) missing values Missing
hardship_length has 42536 (100.0%) missing values Missing
hardship_dpd has 42536 (100.0%) missing values Missing
hardship_loan_status has 42536 (100.0%) missing values Missing
orig_projected_additional_accrued_interest has 42536 (100.0%) missing values Missing
hardship_payoff_balance_amount has 42536 (100.0%) missing values Missing
hardship_last_payment_amount has 42536 (100.0%) missing values Missing
debt_settlement_flag_date has 42376 (99.6%) missing values Missing
settlement_status has 42376 (99.6%) missing values Missing
settlement_date has 42376 (99.6%) missing values Missing
settlement_amount has 42376 (99.6%) missing values Missing
settlement_percentage has 42376 (99.6%) missing values Missing
settlement_term has 42376 (99.6%) missing values Missing
annual_inc is highly skewed (γ1 = 29.03489277) Skewed
collection_recovery_fee is highly skewed (γ1 = 22.41007601) Skewed
url is uniformly distributed Uniform
id is an unsupported type, check if it needs cleaning or further analysis Unsupported
member_id is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_last_major_derog is an unsupported type, check if it needs cleaning or further analysis Unsupported
annual_inc_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
dti_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
verification_status_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
tot_coll_amt is an unsupported type, check if it needs cleaning or further analysis Unsupported
tot_cur_bal is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_acc_6m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_act_il is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_il_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_il_24m is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_rcnt_il is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_bal_il is an unsupported type, check if it needs cleaning or further analysis Unsupported
il_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_rv_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
open_rv_24m is an unsupported type, check if it needs cleaning or further analysis Unsupported
max_bal_bc is an unsupported type, check if it needs cleaning or further analysis Unsupported
all_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_rev_hi_lim is an unsupported type, check if it needs cleaning or further analysis Unsupported
inq_fi is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_cu_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
inq_last_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
acc_open_past_24mths is an unsupported type, check if it needs cleaning or further analysis Unsupported
avg_cur_bal is an unsupported type, check if it needs cleaning or further analysis Unsupported
bc_open_to_buy is an unsupported type, check if it needs cleaning or further analysis Unsupported
bc_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_old_il_acct is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_old_rev_tl_op is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_rcnt_rev_tl_op is an unsupported type, check if it needs cleaning or further analysis Unsupported
mo_sin_rcnt_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
mort_acc is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_bc is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_bc_dlq is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_inq is an unsupported type, check if it needs cleaning or further analysis Unsupported
mths_since_recent_revol_delinq is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_accts_ever_120_pd is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_actv_bc_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_actv_rev_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_bc_sats is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_bc_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_il_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_op_rev_tl is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_rev_accts is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_rev_tl_bal_gt_0 is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_sats is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_120dpd_2m is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_30dpd is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_90g_dpd_24m is an unsupported type, check if it needs cleaning or further analysis Unsupported
num_tl_op_past_12m is an unsupported type, check if it needs cleaning or further analysis Unsupported
pct_tl_nvr_dlq is an unsupported type, check if it needs cleaning or further analysis Unsupported
percent_bc_gt_75 is an unsupported type, check if it needs cleaning or further analysis Unsupported
tot_hi_cred_lim is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_bal_ex_mort is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_bc_limit is an unsupported type, check if it needs cleaning or further analysis Unsupported
total_il_high_credit_limit is an unsupported type, check if it needs cleaning or further analysis Unsupported
revol_bal_joint is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_fico_range_low is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_fico_range_high is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_earliest_cr_line is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_inq_last_6mths is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_mort_acc is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_open_acc is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_revol_util is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_open_act_il is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_num_rev_accts is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_chargeoff_within_12_mths is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_collections_12_mths_ex_med is an unsupported type, check if it needs cleaning or further analysis Unsupported
sec_app_mths_since_last_major_derog is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_type is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_reason is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_status is an unsupported type, check if it needs cleaning or further analysis Unsupported
deferral_term is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_amount is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_start_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_end_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
payment_plan_start_date is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_length is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_dpd is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_loan_status is an unsupported type, check if it needs cleaning or further analysis Unsupported
orig_projected_additional_accrued_interest is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_payoff_balance_amount is an unsupported type, check if it needs cleaning or further analysis Unsupported
hardship_last_payment_amount is an unsupported type, check if it needs cleaning or further analysis Unsupported
delinq_2yrs has 37771 (88.8%) zeros Zeros
inq_last_6mths has 19657 (46.2%) zeros Zeros
mths_since_last_delinq has 27748 (65.2%) zeros Zeros
mths_since_last_record has 1275 (3.0%) zeros Zeros
pub_rec has 40130 (94.3%) zeros Zeros
revol_bal has 1119 (2.6%) zeros Zeros
total_rec_late_fee has 40145 (94.4%) zeros Zeros
recoveries has 36176 (85.0%) zeros Zeros
collection_recovery_fee has 38207 (89.8%) zeros Zeros
last_fico_range_low has 795 (1.9%) zeros Zeros

Reproduction

Analysis started2021-03-24 08:34:11.327887
Analysis finished2021-03-24 08:35:56.943725
Duration1 minute and 45.62 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

id
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size332.4 KiB

member_id
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct898
Distinct (%)2.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11089.72258
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:35:57.003715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15200
median9700
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9800

Descriptive statistics

Standard deviation7410.938391
Coefficient of variation (CV)0.6682708549
Kurtosis0.7858727169
Mean11089.72258
Median Absolute Deviation (MAD)4700
Skewness1.064970449
Sum471701350
Variance54922007.83
MonotocityNot monotonic
2021-03-24T09:35:57.101122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100003016
 
7.1%
120002439
 
5.7%
50002260
 
5.3%
60002037
 
4.8%
150002012
 
4.7%
200001724
 
4.1%
80001699
 
4.0%
250001499
 
3.5%
40001230
 
2.9%
30001134
 
2.7%
Other values (888)23485
55.2%
ValueCountFrequency (%)
50011
< 0.1%
5501
 
< 0.1%
6006
< 0.1%
7003
 
< 0.1%
7251
 
< 0.1%
ValueCountFrequency (%)
35000685
1.6%
348002
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%
344755
 
< 0.1%

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1051
Distinct (%)2.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10821.58575
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:35:57.212161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2275
Q15000
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation7146.914675
Coefficient of variation (CV)0.6604313673
Kurtosis0.9480112486
Mean10821.58575
Median Absolute Deviation (MAD)4600
Skewness1.085767061
Sum460296150
Variance51078389.37
MonotocityNot monotonic
2021-03-24T09:35:57.305545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002924
 
6.9%
120002347
 
5.5%
50002247
 
5.3%
60002023
 
4.8%
150001897
 
4.5%
80001686
 
4.0%
200001546
 
3.6%
40001230
 
2.9%
250001224
 
2.9%
30001125
 
2.6%
Other values (1041)24286
57.1%
ValueCountFrequency (%)
50011
< 0.1%
5501
 
< 0.1%
6006
< 0.1%
7003
 
< 0.1%
7251
 
< 0.1%
ValueCountFrequency (%)
35000559
1.3%
348001
 
< 0.1%
346752
 
< 0.1%
345251
 
< 0.1%
344754
 
< 0.1%

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9248
Distinct (%)21.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean10139.93878
Minimum0
Maximum35000
Zeros233
Zeros (%)0.5%
Memory size332.4 KiB
2021-03-24T09:35:57.406985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1500
Q14950
median8500
Q314000
95-th percentile24575
Maximum35000
Range35000
Interquartile range (IQR)9050

Descriptive statistics

Standard deviation7131.598014
Coefficient of variation (CV)0.7033176595
Kurtosis1.070262642
Mean10139.93878
Median Absolute Deviation (MAD)4223.770511
Skewness1.104831459
Sum431302296.2
Variance50859690.23
MonotocityNot monotonic
2021-03-24T09:35:57.498137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001369
 
3.2%
100001302
 
3.1%
60001240
 
2.9%
120001084
 
2.5%
8000929
 
2.2%
3000851
 
2.0%
4000850
 
2.0%
15000664
 
1.6%
7000615
 
1.4%
2000486
 
1.1%
Other values (9238)33145
77.9%
ValueCountFrequency (%)
0233
0.5%
0.0001210981081
 
< 0.1%
0.0001853694011
 
< 0.1%
0.0002420555111
 
< 0.1%
0.0005311330691
 
< 0.1%
ValueCountFrequency (%)
35000135
0.3%
34997.352451
 
< 0.1%
34993.655391
 
< 0.1%
34993.325711
 
< 0.1%
34993.263061
 
< 0.1%

term
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
36 months
31534 
60 months
11001 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters425350
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months
ValueCountFrequency (%)
36 months31534
74.1%
60 months11001
 
25.9%
(Missing)1
 
< 0.1%
2021-03-24T09:35:57.655264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:35:57.704457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
months42535
50.0%
3631534
37.1%
6011001
 
12.9%

Most occurring characters

ValueCountFrequency (%)
85070
20.0%
642535
10.0%
m42535
10.0%
o42535
10.0%
n42535
10.0%
t42535
10.0%
h42535
10.0%
s42535
10.0%
331534
 
7.4%
011001
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter255210
60.0%
Space Separator85070
 
20.0%
Decimal Number85070
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
m42535
16.7%
o42535
16.7%
n42535
16.7%
t42535
16.7%
h42535
16.7%
s42535
16.7%
ValueCountFrequency (%)
642535
50.0%
331534
37.1%
011001
 
12.9%
ValueCountFrequency (%)
85070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin255210
60.0%
Common170140
40.0%

Most frequent character per script

ValueCountFrequency (%)
m42535
16.7%
o42535
16.7%
n42535
16.7%
t42535
16.7%
h42535
16.7%
s42535
16.7%
ValueCountFrequency (%)
85070
50.0%
642535
25.0%
331534
 
18.5%
011001
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII425350
100.0%

Most frequent character per block

ValueCountFrequency (%)
85070
20.0%
642535
10.0%
m42535
10.0%
o42535
10.0%
n42535
10.0%
t42535
10.0%
h42535
10.0%
s42535
10.0%
331534
 
7.4%
011001
 
2.6%

int_rate
Categorical

HIGH CARDINALITY

Distinct394
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
10.99%
 
970
11.49%
 
837
13.49%
 
832
7.51%
 
787
7.88%
 
742
Other values (389)
38367 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters297745
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row 10.65%
2nd row 15.27%
3rd row 15.96%
4th row 13.49%
5th row 12.69%
ValueCountFrequency (%)
10.99%970
 
2.3%
11.49%837
 
2.0%
13.49%832
 
2.0%
7.51%787
 
1.9%
7.88%742
 
1.7%
7.49%656
 
1.5%
11.71%609
 
1.4%
9.99%607
 
1.4%
7.90%582
 
1.4%
5.42%573
 
1.3%
Other values (384)35340
83.1%
2021-03-24T09:35:57.865424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.99970
 
2.3%
11.49837
 
2.0%
13.49832
 
2.0%
7.51787
 
1.9%
7.88742
 
1.7%
7.49656
 
1.5%
11.71609
 
1.4%
9.99607
 
1.4%
7.90582
 
1.4%
5.42573
 
1.3%
Other values (384)35340
83.1%

Most occurring characters

ValueCountFrequency (%)
54877
18.4%
.42535
14.3%
%42535
14.3%
141594
14.0%
922648
7.6%
213727
 
4.6%
612870
 
4.3%
712841
 
4.3%
412024
 
4.0%
310961
 
3.7%
Other values (3)31133
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157798
53.0%
Other Punctuation85070
28.6%
Space Separator54877
 
18.4%

Most frequent character per category

ValueCountFrequency (%)
141594
26.4%
922648
14.4%
213727
 
8.7%
612870
 
8.2%
712841
 
8.1%
412024
 
7.6%
310961
 
6.9%
510910
 
6.9%
810329
 
6.5%
09894
 
6.3%
ValueCountFrequency (%)
.42535
50.0%
%42535
50.0%
ValueCountFrequency (%)
54877
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common297745
100.0%

Most frequent character per script

ValueCountFrequency (%)
54877
18.4%
.42535
14.3%
%42535
14.3%
141594
14.0%
922648
7.6%
213727
 
4.6%
612870
 
4.3%
712841
 
4.3%
412024
 
4.0%
310961
 
3.7%
Other values (3)31133
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII297745
100.0%

Most frequent character per block

ValueCountFrequency (%)
54877
18.4%
.42535
14.3%
%42535
14.3%
141594
14.0%
922648
7.6%
213727
 
4.6%
612870
 
4.3%
712841
 
4.3%
412024
 
4.0%
310961
 
3.7%
Other values (3)31133
10.5%

installment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16459
Distinct (%)38.7%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean322.6230627
Minimum15.67
Maximum1305.19
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:35:57.951744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile69.681
Q1165.52
median277.69
Q3428.18
95-th percentile762.08
Maximum1305.19
Range1289.52
Interquartile range (IQR)262.66

Descriptive statistics

Standard deviation208.9272165
Coefficient of variation (CV)0.6475892169
Kurtosis1.20456977
Mean322.6230627
Median Absolute Deviation (MAD)122.18
Skewness1.125240144
Sum13722771.97
Variance43650.58179
MonotocityNot monotonic
2021-03-24T09:35:58.049840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1168
 
0.2%
180.9659
 
0.1%
311.0254
 
0.1%
150.848
 
0.1%
368.4546
 
0.1%
372.1245
 
0.1%
330.7643
 
0.1%
317.7242
 
0.1%
339.3142
 
0.1%
186.6141
 
0.1%
Other values (16449)42047
98.9%
ValueCountFrequency (%)
15.671
< 0.1%
15.691
< 0.1%
15.751
< 0.1%
15.761
< 0.1%
15.911
< 0.1%
ValueCountFrequency (%)
1305.191
< 0.1%
1302.691
< 0.1%
1295.211
< 0.1%
1288.12
< 0.1%
1283.51
< 0.1%

grade
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
B
12389 
A
10183 
C
8740 
D
6016 
E
3394 
Other values (2)
1813 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42535
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB
ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%
(Missing)1
 
< 0.1%
2021-03-24T09:35:58.223945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:35:58.277006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
b12389
29.1%
a10183
23.9%
c8740
20.5%
d6016
14.1%
e3394
 
8.0%
f1301
 
3.1%
g512
 
1.2%

Most occurring characters

ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42535
100.0%

Most frequent character per category

ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin42535
100.0%

Most frequent character per script

ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII42535
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%

sub_grade
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
B3
2997 
A4
2905 
B5
 
2807
A5
 
2793
B4
 
2590
Other values (30)
28443 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters85070
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5
ValueCountFrequency (%)
B32997
 
7.0%
A42905
 
6.8%
B52807
 
6.6%
A52793
 
6.6%
B42590
 
6.1%
C12264
 
5.3%
C22157
 
5.1%
B22113
 
5.0%
B11882
 
4.4%
A31823
 
4.3%
Other values (25)18204
42.8%
2021-03-24T09:35:58.454881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b32997
 
7.0%
a42905
 
6.8%
b52807
 
6.6%
a52793
 
6.6%
b42590
 
6.1%
c12264
 
5.3%
c22157
 
5.1%
b22113
 
5.0%
b11882
 
4.4%
a31823
 
4.3%
Other values (25)18204
42.8%

Most occurring characters

ValueCountFrequency (%)
B12389
14.6%
A10183
12.0%
48867
10.4%
38783
10.3%
C8740
10.3%
58646
10.2%
28481
10.0%
17758
9.1%
D6016
7.1%
E3394
 
4.0%
Other values (2)1813
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42535
50.0%
Decimal Number42535
50.0%

Most frequent character per category

ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%
ValueCountFrequency (%)
48867
20.8%
38783
20.6%
58646
20.3%
28481
19.9%
17758
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin42535
50.0%
Common42535
50.0%

Most frequent character per script

ValueCountFrequency (%)
B12389
29.1%
A10183
23.9%
C8740
20.5%
D6016
14.1%
E3394
 
8.0%
F1301
 
3.1%
G512
 
1.2%
ValueCountFrequency (%)
48867
20.8%
38783
20.6%
58646
20.3%
28481
19.9%
17758
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII85070
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12389
14.6%
A10183
12.0%
48867
10.4%
38783
10.3%
C8740
10.3%
58646
10.2%
28481
10.0%
17758
9.1%
D6016
7.1%
E3394
 
4.0%
Other values (2)1813
 
2.1%

emp_title
Categorical

HIGH CARDINALITY
MISSING

Distinct30658
Distinct (%)76.8%
Missing2627
Missing (%)6.2%
Memory size332.4 KiB
US Army
 
139
Bank of America
 
115
IBM
 
72
AT&T
 
61
Kaiser Permanente
 
61
Other values (30653)
39461 

Length

Max length78
Median length18
Mean length18.34325591
Min length2

Characters and Unicode

Total characters732061
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27187 ?
Unique (%)68.1%

Sample

1st rowRyder
2nd rowAIR RESOURCES BOARD
3rd rowUniversity Medical Group
4th rowVeolia Transportaton
5th rowSouthern Star Photography
ValueCountFrequency (%)
US Army139
 
0.3%
Bank of America115
 
0.3%
IBM72
 
0.2%
AT&T61
 
0.1%
Kaiser Permanente61
 
0.1%
UPS58
 
0.1%
Wells Fargo57
 
0.1%
USAF56
 
0.1%
US Air Force55
 
0.1%
Self Employed49
 
0.1%
Other values (30648)39186
92.1%
(Missing)2627
 
6.2%
2021-03-24T09:35:58.708599image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc3403
 
3.2%
of3200
 
3.0%
1283
 
1.2%
and1035
 
1.0%
services868
 
0.8%
center865
 
0.8%
bank850
 
0.8%
county846
 
0.8%
the797
 
0.7%
school794
 
0.7%
Other values (19749)93627
87.0%

Most occurring characters

ValueCountFrequency (%)
69179
 
9.4%
e59921
 
8.2%
a46810
 
6.4%
n45589
 
6.2%
o45451
 
6.2%
i43260
 
5.9%
r42878
 
5.9%
t41183
 
5.6%
s32500
 
4.4%
l27700
 
3.8%
Other values (86)277590
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter523195
71.5%
Uppercase Letter127793
 
17.5%
Space Separator69179
 
9.4%
Other Punctuation9393
 
1.3%
Dash Punctuation1098
 
0.1%
Decimal Number1026
 
0.1%
Open Punctuation172
 
< 0.1%
Close Punctuation169
 
< 0.1%
Math Symbol23
 
< 0.1%
Currency Symbol3
 
< 0.1%
Other values (5)10
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C15540
 
12.2%
S14246
 
11.1%
A9494
 
7.4%
I8073
 
6.3%
M6955
 
5.4%
P6470
 
5.1%
T6122
 
4.8%
L5978
 
4.7%
E5635
 
4.4%
D5406
 
4.2%
Other values (18)43874
34.3%
ValueCountFrequency (%)
e59921
11.5%
a46810
8.9%
n45589
8.7%
o45451
8.7%
i43260
 
8.3%
r42878
 
8.2%
t41183
 
7.9%
s32500
 
6.2%
l27700
 
5.3%
c24704
 
4.7%
Other values (17)113199
21.6%
ValueCountFrequency (%)
.4560
48.5%
,2341
24.9%
&1381
 
14.7%
'688
 
7.3%
/334
 
3.6%
#37
 
0.4%
@10
 
0.1%
!9
 
0.1%
:9
 
0.1%
"8
 
0.1%
Other values (5)16
 
0.2%
ValueCountFrequency (%)
1204
19.9%
2170
16.6%
3162
15.8%
0105
10.2%
499
9.6%
575
 
7.3%
968
 
6.6%
661
 
5.9%
747
 
4.6%
835
 
3.4%
ValueCountFrequency (%)
+20
87.0%
|2
 
8.7%
<1
 
4.3%
ValueCountFrequency (%)
(171
99.4%
[1
 
0.6%
ValueCountFrequency (%)
€1
50.0%
ƒ1
50.0%
ValueCountFrequency (%)
$2
66.7%
¢1
33.3%
ValueCountFrequency (%)
69179
100.0%
ValueCountFrequency (%)
-1098
100.0%
ValueCountFrequency (%)
)169
100.0%
ValueCountFrequency (%)
©2
100.0%
ValueCountFrequency (%)
`2
100.0%
ValueCountFrequency (%)
_2
100.0%
ValueCountFrequency (%)
²2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin650988
88.9%
Common81073
 
11.1%

Most frequent character per script

ValueCountFrequency (%)
e59921
 
9.2%
a46810
 
7.2%
n45589
 
7.0%
o45451
 
7.0%
i43260
 
6.6%
r42878
 
6.6%
t41183
 
6.3%
s32500
 
5.0%
l27700
 
4.3%
c24704
 
3.8%
Other values (45)240992
37.0%
ValueCountFrequency (%)
69179
85.3%
.4560
 
5.6%
,2341
 
2.9%
&1381
 
1.7%
-1098
 
1.4%
'688
 
0.8%
/334
 
0.4%
1204
 
0.3%
(171
 
0.2%
2170
 
0.2%
Other values (31)947
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII732047
> 99.9%
None14
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
69179
 
9.5%
e59921
 
8.2%
a46810
 
6.4%
n45589
 
6.2%
o45451
 
6.2%
i43260
 
5.9%
r42878
 
5.9%
t41183
 
5.6%
s32500
 
4.4%
l27700
 
3.8%
Other values (77)277576
37.9%
ValueCountFrequency (%)
Ã3
21.4%
©2
14.3%
Â2
14.3%
²2
14.3%
â1
 
7.1%
€1
 
7.1%
¢1
 
7.1%
ƒ1
 
7.1%
¡1
 
7.1%

emp_length
Categorical

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)< 0.1%
Missing1113
Missing (%)2.6%
Memory size332.4 KiB
10+ years
9369 
< 1 year
5062 
2 years
4743 
3 years
4364 
4 years
3649 
Other values (6)
14236 

Length

Max length9
Median length7
Mean length7.487772494
Min length6

Characters and Unicode

Total characters310166
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row1 year
ValueCountFrequency (%)
10+ years9369
22.0%
< 1 year5062
11.9%
2 years4743
11.2%
3 years4364
10.3%
4 years3649
 
8.6%
1 year3595
 
8.5%
5 years3458
 
8.1%
6 years2375
 
5.6%
7 years1875
 
4.4%
8 years1592
 
3.7%
2021-03-24T09:35:58.898487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years32766
37.3%
109369
 
10.7%
18657
 
9.8%
year8657
 
9.8%
5062
 
5.8%
24743
 
5.4%
34364
 
5.0%
43649
 
4.2%
53458
 
3.9%
62375
 
2.7%
Other values (3)4808
 
5.5%

Most occurring characters

ValueCountFrequency (%)
46485
15.0%
y41423
13.4%
e41423
13.4%
a41423
13.4%
r41423
13.4%
s32766
10.6%
118026
 
5.8%
09369
 
3.0%
+9369
 
3.0%
<5062
 
1.6%
Other values (8)23397
7.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter198458
64.0%
Decimal Number50792
 
16.4%
Space Separator46485
 
15.0%
Math Symbol14431
 
4.7%

Most frequent character per category

ValueCountFrequency (%)
118026
35.5%
09369
18.4%
24743
 
9.3%
34364
 
8.6%
43649
 
7.2%
53458
 
6.8%
62375
 
4.7%
71875
 
3.7%
81592
 
3.1%
91341
 
2.6%
ValueCountFrequency (%)
y41423
20.9%
e41423
20.9%
a41423
20.9%
r41423
20.9%
s32766
16.5%
ValueCountFrequency (%)
+9369
64.9%
<5062
35.1%
ValueCountFrequency (%)
46485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin198458
64.0%
Common111708
36.0%

Most frequent character per script

ValueCountFrequency (%)
46485
41.6%
118026
 
16.1%
09369
 
8.4%
+9369
 
8.4%
<5062
 
4.5%
24743
 
4.2%
34364
 
3.9%
43649
 
3.3%
53458
 
3.1%
62375
 
2.1%
Other values (3)4808
 
4.3%
ValueCountFrequency (%)
y41423
20.9%
e41423
20.9%
a41423
20.9%
r41423
20.9%
s32766
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII310166
100.0%

Most frequent character per block

ValueCountFrequency (%)
46485
15.0%
y41423
13.4%
e41423
13.4%
a41423
13.4%
r41423
13.4%
s32766
10.6%
118026
 
5.8%
09369
 
3.0%
+9369
 
3.0%
<5062
 
1.6%
Other values (8)23397
7.5%

home_ownership
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
RENT
20181 
MORTGAGE
18959 
OWN
3251 
OTHER
 
136
NONE
 
8

Length

Max length8
Median length4
Mean length5.709674386
Min length3

Characters and Unicode

Total characters242861
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT
ValueCountFrequency (%)
RENT20181
47.4%
MORTGAGE18959
44.6%
OWN3251
 
7.6%
OTHER136
 
0.3%
NONE8
 
< 0.1%
(Missing)1
 
< 0.1%
2021-03-24T09:35:59.054278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:35:59.103431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
rent20181
47.4%
mortgage18959
44.6%
own3251
 
7.6%
other136
 
0.3%
none8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E39284
16.2%
R39276
16.2%
T39276
16.2%
G37918
15.6%
N23448
9.7%
O22354
9.2%
M18959
7.8%
A18959
7.8%
W3251
 
1.3%
H136
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter242861
100.0%

Most frequent character per category

ValueCountFrequency (%)
E39284
16.2%
R39276
16.2%
T39276
16.2%
G37918
15.6%
N23448
9.7%
O22354
9.2%
M18959
7.8%
A18959
7.8%
W3251
 
1.3%
H136
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin242861
100.0%

Most frequent character per script

ValueCountFrequency (%)
E39284
16.2%
R39276
16.2%
T39276
16.2%
G37918
15.6%
N23448
9.7%
O22354
9.2%
M18959
7.8%
A18959
7.8%
W3251
 
1.3%
H136
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII242861
100.0%

Most frequent character per block

ValueCountFrequency (%)
E39284
16.2%
R39276
16.2%
T39276
16.2%
G37918
15.6%
N23448
9.7%
O22354
9.2%
M18959
7.8%
A18959
7.8%
W3251
 
1.3%
H136
 
0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct5597
Distinct (%)13.2%
Missing5
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean69136.55642
Minimum1896
Maximum6000000
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:35:59.182888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1896
5-th percentile24000
Q140000
median59000
Q382500
95-th percentile144000
Maximum6000000
Range5998104
Interquartile range (IQR)42500

Descriptive statistics

Standard deviation64096.34972
Coefficient of variation (CV)0.9270978052
Kurtosis2117.344597
Mean69136.55642
Median Absolute Deviation (MAD)20000
Skewness29.03489277
Sum2940446881
Variance4108342047
MonotocityNot monotonic
2021-03-24T09:35:59.277354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600001591
 
3.7%
500001119
 
2.6%
40000935
 
2.2%
45000898
 
2.1%
30000884
 
2.1%
75000865
 
2.0%
65000840
 
2.0%
70000790
 
1.9%
48000766
 
1.8%
80000718
 
1.7%
Other values (5587)33125
77.9%
ValueCountFrequency (%)
18961
< 0.1%
20001
< 0.1%
33001
< 0.1%
35001
< 0.1%
36001
< 0.1%
ValueCountFrequency (%)
60000001
< 0.1%
39000001
< 0.1%
20397841
< 0.1%
19000001
< 0.1%
17820001
< 0.1%

verification_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
Not Verified
18758 
Verified
13471 
Source Verified
10306 

Length

Max length15
Median length12
Mean length11.46006818
Min length8

Characters and Unicode

Total characters487454
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified
ValueCountFrequency (%)
Not Verified18758
44.1%
Verified13471
31.7%
Source Verified10306
24.2%
(Missing)1
 
< 0.1%
2021-03-24T09:35:59.443510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:35:59.497700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
verified42535
59.4%
not18758
26.2%
source10306
 
14.4%

Most occurring characters

ValueCountFrequency (%)
e95376
19.6%
i85070
17.5%
r52841
10.8%
V42535
8.7%
f42535
8.7%
d42535
8.7%
o29064
 
6.0%
29064
 
6.0%
N18758
 
3.8%
t18758
 
3.8%
Other values (3)30918
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter386791
79.3%
Uppercase Letter71599
 
14.7%
Space Separator29064
 
6.0%

Most frequent character per category

ValueCountFrequency (%)
e95376
24.7%
i85070
22.0%
r52841
13.7%
f42535
11.0%
d42535
11.0%
o29064
 
7.5%
t18758
 
4.8%
u10306
 
2.7%
c10306
 
2.7%
ValueCountFrequency (%)
V42535
59.4%
N18758
26.2%
S10306
 
14.4%
ValueCountFrequency (%)
29064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin458390
94.0%
Common29064
 
6.0%

Most frequent character per script

ValueCountFrequency (%)
e95376
20.8%
i85070
18.6%
r52841
11.5%
V42535
9.3%
f42535
9.3%
d42535
9.3%
o29064
 
6.3%
N18758
 
4.1%
t18758
 
4.1%
S10306
 
2.2%
Other values (2)20612
 
4.5%
ValueCountFrequency (%)
29064
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII487454
100.0%

Most frequent character per block

ValueCountFrequency (%)
e95376
19.6%
i85070
17.5%
r52841
10.8%
V42535
8.7%
f42535
8.7%
d42535
8.7%
o29064
 
6.0%
29064
 
6.0%
N18758
 
3.8%
t18758
 
3.8%
Other values (3)30918
 
6.3%

issue_d
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct55
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
Dec-2011
 
2267
Nov-2011
 
2232
Oct-2011
 
2118
Sep-2011
 
2067
Aug-2011
 
1934
Other values (50)
31917 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters340280
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDec-2011
2nd rowDec-2011
3rd rowDec-2011
4th rowDec-2011
5th rowDec-2011
ValueCountFrequency (%)
Dec-20112267
 
5.3%
Nov-20112232
 
5.2%
Oct-20112118
 
5.0%
Sep-20112067
 
4.9%
Aug-20111934
 
4.5%
Jul-20111875
 
4.4%
Jun-20111835
 
4.3%
May-20111704
 
4.0%
Apr-20111563
 
3.7%
Mar-20111448
 
3.4%
Other values (45)23492
55.2%
2021-03-24T09:35:59.656142image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-20112267
 
5.3%
nov-20112232
 
5.2%
oct-20112118
 
5.0%
sep-20112067
 
4.9%
aug-20111934
 
4.5%
jul-20111875
 
4.4%
jun-20111835
 
4.3%
may-20111704
 
4.0%
apr-20111563
 
3.7%
mar-20111448
 
3.4%
Other values (45)23492
55.2%

Most occurring characters

ValueCountFrequency (%)
063349
18.6%
155979
16.5%
-42535
12.5%
242535
12.5%
e11146
 
3.3%
u10917
 
3.2%
J9804
 
2.9%
c8866
 
2.6%
a8785
 
2.6%
p6940
 
2.0%
Other values (19)79424
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number170140
50.0%
Lowercase Letter85070
25.0%
Uppercase Letter42535
 
12.5%
Dash Punctuation42535
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e11146
13.1%
u10917
12.8%
c8866
10.4%
a8785
10.3%
p6940
8.2%
n6110
7.2%
r6069
7.1%
o4439
 
5.2%
v4439
 
5.2%
t4181
 
4.9%
Other values (4)13178
15.5%
ValueCountFrequency (%)
J9804
23.0%
A6796
16.0%
M6169
14.5%
D4685
11.0%
N4439
10.4%
O4181
9.8%
S3873
 
9.1%
F2588
 
6.1%
ValueCountFrequency (%)
063349
37.2%
155979
32.9%
242535
25.0%
95281
 
3.1%
82393
 
1.4%
7603
 
0.4%
ValueCountFrequency (%)
-42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common212675
62.5%
Latin127605
37.5%

Most frequent character per script

ValueCountFrequency (%)
e11146
 
8.7%
u10917
 
8.6%
J9804
 
7.7%
c8866
 
6.9%
a8785
 
6.9%
p6940
 
5.4%
A6796
 
5.3%
M6169
 
4.8%
n6110
 
4.8%
r6069
 
4.8%
Other values (12)46003
36.1%
ValueCountFrequency (%)
063349
29.8%
155979
26.3%
-42535
20.0%
242535
20.0%
95281
 
2.5%
82393
 
1.1%
7603
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII340280
100.0%

Most frequent character per block

ValueCountFrequency (%)
063349
18.6%
155979
16.5%
-42535
12.5%
242535
12.5%
e11146
 
3.3%
u10917
 
3.2%
J9804
 
2.9%
c8866
 
2.6%
a8785
 
2.6%
p6940
 
2.0%
Other values (19)79424
23.3%

loan_status
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
Fully Paid
34116 
Charged Off
5670 
Does not meet the credit policy. Status:Fully Paid
 
1988
Does not meet the credit policy. Status:Charged Off
 
761

Length

Max length51
Median length10
Mean length12.73635829
Min length10

Characters and Unicode

Total characters541741
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid
ValueCountFrequency (%)
Fully Paid34116
80.2%
Charged Off5670
 
13.3%
Does not meet the credit policy. Status:Fully Paid1988
 
4.7%
Does not meet the credit policy. Status:Charged Off761
 
1.8%
(Missing)1
 
< 0.1%
2021-03-24T09:35:59.810562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:35:59.862227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
paid36104
35.5%
fully34116
33.6%
off6431
 
6.3%
charged5670
 
5.6%
credit2749
 
2.7%
meet2749
 
2.7%
the2749
 
2.7%
policy2749
 
2.7%
not2749
 
2.7%
does2749
 
2.7%
Other values (2)2749
 
2.7%

Most occurring characters

ValueCountFrequency (%)
l74957
13.8%
59029
10.9%
a45284
8.4%
d45284
8.4%
i41602
 
7.7%
u38853
 
7.2%
y38853
 
7.2%
F36104
 
6.7%
P36104
 
6.7%
e20176
 
3.7%
Other values (17)105495
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter386646
71.4%
Uppercase Letter90568
 
16.7%
Space Separator59029
 
10.9%
Other Punctuation5498
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
l74957
19.4%
a45284
11.7%
d45284
11.7%
i41602
10.8%
u38853
10.0%
y38853
10.0%
e20176
 
5.2%
t16494
 
4.3%
f12862
 
3.3%
h9180
 
2.4%
Other values (8)43101
11.1%
ValueCountFrequency (%)
F36104
39.9%
P36104
39.9%
C6431
 
7.1%
O6431
 
7.1%
D2749
 
3.0%
S2749
 
3.0%
ValueCountFrequency (%)
.2749
50.0%
:2749
50.0%
ValueCountFrequency (%)
59029
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin477214
88.1%
Common64527
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
l74957
15.7%
a45284
9.5%
d45284
9.5%
i41602
8.7%
u38853
8.1%
y38853
8.1%
F36104
7.6%
P36104
7.6%
e20176
 
4.2%
t16494
 
3.5%
Other values (14)83503
17.5%
ValueCountFrequency (%)
59029
91.5%
.2749
 
4.3%
:2749
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII541741
100.0%

Most frequent character per block

ValueCountFrequency (%)
l74957
13.8%
59029
10.9%
a45284
8.4%
d45284
8.4%
i41602
 
7.7%
u38853
 
7.2%
y38853
 
7.2%
F36104
 
6.7%
P36104
 
6.7%
e20176
 
3.7%
Other values (17)105495
19.5%

pymnt_plan
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size83.2 KiB
False
42535 
(Missing)
 
1
ValueCountFrequency (%)
False42535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:35:59.908316image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

url
Categorical

HIGH CARDINALITY
UNIFORM

Distinct42535
Distinct (%)100.0%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
https://lendingclub.com/browse/loanDetail.action?loan_id=460307
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=483102
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=764195
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=993479
 
1
https://lendingclub.com/browse/loanDetail.action?loan_id=1049647
 
1
Other values (42530)
42530 

Length

Max length64
Median length63
Mean length63.10008229
Min length62

Characters and Unicode

Total characters2683962
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42535 ?
Unique (%)100.0%

Sample

1st rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501
2nd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430
3rd rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175
4th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863
5th rowhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358
ValueCountFrequency (%)
https://lendingclub.com/browse/loanDetail.action?loan_id=4603071
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=4831021
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=7641951
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=9934791
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=10496471
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=6330561
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=7502121
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=7562971
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=8885561
 
< 0.1%
https://lendingclub.com/browse/loanDetail.action?loan_id=8687591
 
< 0.1%
Other values (42525)42525
> 99.9%
2021-03-24T09:36:00.129650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://lendingclub.com/browse/loandetail.action?loan_id=5033931
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=3472281
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=6742251
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=3633671
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=5396071
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=5653871
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=7474831
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=3659271
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=10145731
 
< 0.1%
https://lendingclub.com/browse/loandetail.action?loan_id=4141341
 
< 0.1%
Other values (42525)42525
> 99.9%

Most occurring characters

ValueCountFrequency (%)
l212675
 
7.9%
n212675
 
7.9%
o212675
 
7.9%
t170140
 
6.3%
/170140
 
6.3%
i170140
 
6.3%
a170140
 
6.3%
e127605
 
4.8%
c127605
 
4.8%
s85070
 
3.2%
Other values (25)1025097
38.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1956610
72.9%
Other Punctuation340280
 
12.7%
Decimal Number259467
 
9.7%
Uppercase Letter42535
 
1.6%
Connector Punctuation42535
 
1.6%
Math Symbol42535
 
1.6%

Most frequent character per category

ValueCountFrequency (%)
l212675
10.9%
n212675
10.9%
o212675
10.9%
t170140
8.7%
i170140
8.7%
a170140
8.7%
e127605
 
6.5%
c127605
 
6.5%
s85070
 
4.3%
d85070
 
4.3%
Other values (8)382815
19.6%
ValueCountFrequency (%)
528614
11.0%
628238
10.9%
427659
10.7%
727532
10.6%
827202
10.5%
125851
10.0%
025228
9.7%
324043
9.3%
923116
8.9%
221984
8.5%
ValueCountFrequency (%)
/170140
50.0%
.85070
25.0%
:42535
 
12.5%
?42535
 
12.5%
ValueCountFrequency (%)
D42535
100.0%
ValueCountFrequency (%)
_42535
100.0%
ValueCountFrequency (%)
=42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1999145
74.5%
Common684817
 
25.5%

Most frequent character per script

ValueCountFrequency (%)
l212675
10.6%
n212675
10.6%
o212675
10.6%
t170140
 
8.5%
i170140
 
8.5%
a170140
 
8.5%
e127605
 
6.4%
c127605
 
6.4%
s85070
 
4.3%
d85070
 
4.3%
Other values (9)425350
21.3%
ValueCountFrequency (%)
/170140
24.8%
.85070
12.4%
:42535
 
6.2%
?42535
 
6.2%
_42535
 
6.2%
=42535
 
6.2%
528614
 
4.2%
628238
 
4.1%
427659
 
4.0%
727532
 
4.0%
Other values (6)147424
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2683962
100.0%

Most frequent character per block

ValueCountFrequency (%)
l212675
 
7.9%
n212675
 
7.9%
o212675
 
7.9%
t170140
 
6.3%
/170140
 
6.3%
i170140
 
6.3%
a170140
 
6.3%
e127605
 
4.8%
c127605
 
4.8%
s85070
 
3.2%
Other values (25)1025097
38.2%

desc
Categorical

HIGH CARDINALITY
MISSING

Distinct28963
Distinct (%)99.0%
Missing13294
Missing (%)31.3%
Memory size332.4 KiB
 
225
Debt Consolidation
 
11
Camping Membership
 
8
refinancing
 
5
credit card consolidation
 
3
Other values (28958)
28990 

Length

Max length3988
Median length281
Mean length423.4260311
Min length1

Characters and Unicode

Total characters12381824
Distinct characters142
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28931 ?
Unique (%)98.9%

Sample

1st row Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>
2nd row Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>
3rd row Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>
4th row Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>
5th row Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>
ValueCountFrequency (%)
225
 
0.5%
Debt Consolidation11
 
< 0.1%
Camping Membership8
 
< 0.1%
refinancing5
 
< 0.1%
credit card consolidation3
 
< 0.1%
consolidate debt3
 
< 0.1%
personal loan3
 
< 0.1%
credit card debt consolidation3
 
< 0.1%
Personal Loan3
 
< 0.1%
debt consolidation3
 
< 0.1%
Other values (28953)28975
68.1%
(Missing)13294
31.3%
2021-03-24T09:36:00.387691image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i85087
 
3.8%
to77670
 
3.5%
a60001
 
2.7%
and59305
 
2.7%
the59159
 
2.7%
my55839
 
2.5%
on51981
 
2.3%
39157
 
1.8%
for35488
 
1.6%
have35150
 
1.6%
Other values (56778)1668318
74.9%

Most occurring characters

ValueCountFrequency (%)
2303677
18.6%
e1036186
 
8.4%
a775912
 
6.3%
o767722
 
6.2%
t707444
 
5.7%
n664469
 
5.4%
r634406
 
5.1%
i540140
 
4.4%
s464080
 
3.7%
d428929
 
3.5%
Other values (132)4058859
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8832480
71.3%
Space Separator2303751
 
18.6%
Decimal Number368643
 
3.0%
Other Punctuation350646
 
2.8%
Uppercase Letter329296
 
2.7%
Math Symbol146513
 
1.2%
Currency Symbol18187
 
0.1%
Dash Punctuation14304
 
0.1%
Close Punctuation7970
 
0.1%
Open Punctuation7305
 
0.1%
Other values (7)2729
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
I103385
31.4%
B36481
 
11.1%
T30887
 
9.4%
A17040
 
5.2%
M15514
 
4.7%
C15439
 
4.7%
S10680
 
3.2%
E10134
 
3.1%
W9701
 
2.9%
L9398
 
2.9%
Other values (21)70637
21.5%
ValueCountFrequency (%)
e1036186
11.7%
a775912
 
8.8%
o767722
 
8.7%
t707444
 
8.0%
n664469
 
7.5%
r634406
 
7.2%
i540140
 
6.1%
s464080
 
5.3%
d428929
 
4.9%
l386876
 
4.4%
Other values (18)2426316
27.5%
ValueCountFrequency (%)
.131381
37.5%
/121672
34.7%
,54749
15.6%
'14495
 
4.1%
!7335
 
2.1%
%6191
 
1.8%
:5814
 
1.7%
;3493
 
1.0%
&2753
 
0.8%
"925
 
0.3%
Other values (10)1838
 
0.5%
ValueCountFrequency (%)
1318
60.2%
€426
 
19.5%
™200
 
9.1%
’38
 
1.7%
“38
 
1.7%
‚35
 
1.6%
29
 
1.3%
ƒ27
 
1.2%
œ25
 
1.1%
š15
 
0.7%
Other values (9)39
 
1.8%
ValueCountFrequency (%)
0111941
30.4%
1101613
27.6%
239139
 
10.6%
523226
 
6.3%
319094
 
5.2%
917472
 
4.7%
414581
 
4.0%
614099
 
3.8%
713782
 
3.7%
813696
 
3.7%
ValueCountFrequency (%)
>88381
60.3%
<56071
38.3%
+1055
 
0.7%
=647
 
0.4%
~318
 
0.2%
¬31
 
< 0.1%
|10
 
< 0.1%
ValueCountFrequency (%)
¦97
80.2%
©15
 
12.4%
7
 
5.8%
®2
 
1.7%
ValueCountFrequency (%)
(7258
99.4%
[44
 
0.6%
{3
 
< 0.1%
ValueCountFrequency (%)
)7922
99.4%
]44
 
0.6%
}4
 
0.1%
ValueCountFrequency (%)
-14285
99.9%
10
 
0.1%
9
 
0.1%
ValueCountFrequency (%)
`13
68.4%
^5
 
26.3%
¯1
 
5.3%
ValueCountFrequency (%)
2303677
> 99.9%
 74
 
< 0.1%
ValueCountFrequency (%)
$18113
99.6%
¢74
 
0.4%
ValueCountFrequency (%)
½7
77.8%
¾2
 
22.2%
ValueCountFrequency (%)
90
83.3%
18
 
16.7%
ValueCountFrequency (%)
18
85.7%
3
 
14.3%
ValueCountFrequency (%)
_261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9161776
74.0%
Common3220048
 
26.0%

Most frequent character per script

ValueCountFrequency (%)
2303677
71.5%
.131381
 
4.1%
/121672
 
3.8%
0111941
 
3.5%
1101613
 
3.2%
>88381
 
2.7%
<56071
 
1.7%
,54749
 
1.7%
239139
 
1.2%
523226
 
0.7%
Other values (73)188198
 
5.8%
ValueCountFrequency (%)
e1036186
 
11.3%
a775912
 
8.5%
o767722
 
8.4%
t707444
 
7.7%
n664469
 
7.3%
r634406
 
6.9%
i540140
 
5.9%
s464080
 
5.1%
d428929
 
4.7%
l386876
 
4.2%
Other values (49)2755612
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12379753
> 99.9%
None1886
 
< 0.1%
Punctuation178
 
< 0.1%
Specials7
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2303677
18.6%
e1036186
 
8.4%
a775912
 
6.3%
o767722
 
6.2%
t707444
 
5.7%
n664469
 
5.4%
r634406
 
5.1%
i540140
 
4.4%
s464080
 
3.7%
d428929
 
3.5%
Other values (86)4056788
32.8%
ValueCountFrequency (%)
â453
24.0%
€426
22.6%
™200
10.6%
Â128
 
6.8%
Ã97
 
5.1%
¦97
 
5.1%
¢74
 
3.9%
 74
 
3.9%
’38
 
2.0%
“38
 
2.0%
Other values (27)261
13.8%
ValueCountFrequency (%)
90
50.6%
22
 
12.4%
18
 
10.1%
18
 
10.1%
10
 
5.6%
9
 
5.1%
8
 
4.5%
3
 
1.7%
ValueCountFrequency (%)
7
100.0%

purpose
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
debt_consolidation
19776 
credit_card
5477 
other
4425 
home_improvement
3199 
major_purchase
2311 
Other values (9)
7347 

Length

Max length18
Median length16
Mean length13.69112496
Min length3

Characters and Unicode

Total characters582352
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowother
ValueCountFrequency (%)
debt_consolidation19776
46.5%
credit_card5477
 
12.9%
other4425
 
10.4%
home_improvement3199
 
7.5%
major_purchase2311
 
5.4%
small_business1992
 
4.7%
car1615
 
3.8%
wedding1004
 
2.4%
medical753
 
1.8%
moving629
 
1.5%
Other values (4)1354
 
3.2%
2021-03-24T09:36:00.586664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation19776
46.5%
credit_card5477
 
12.9%
other4425
 
10.4%
home_improvement3199
 
7.5%
major_purchase2311
 
5.4%
small_business1992
 
4.7%
car1615
 
3.8%
wedding1004
 
2.4%
medical753
 
1.8%
moving629
 
1.5%
Other values (4)1354
 
3.2%

Most occurring characters

ValueCountFrequency (%)
o74339
12.8%
d53689
9.2%
t53475
9.2%
i53428
9.2%
n47410
8.1%
e46713
 
8.0%
c36231
 
6.2%
a35985
 
6.2%
_32861
 
5.6%
s30481
 
5.2%
Other values (12)117740
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter549491
94.4%
Connector Punctuation32861
 
5.6%

Most frequent character per category

ValueCountFrequency (%)
o74339
13.5%
d53689
9.8%
t53475
9.7%
i53428
9.7%
n47410
8.6%
e46713
8.5%
c36231
 
6.6%
a35985
 
6.5%
s30481
 
5.5%
l25041
 
4.6%
Other values (11)92699
16.9%
ValueCountFrequency (%)
_32861
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin549491
94.4%
Common32861
 
5.6%

Most frequent character per script

ValueCountFrequency (%)
o74339
13.5%
d53689
9.8%
t53475
9.7%
i53428
9.7%
n47410
8.6%
e46713
8.5%
c36231
 
6.6%
a35985
 
6.5%
s30481
 
5.5%
l25041
 
4.6%
Other values (11)92699
16.9%
ValueCountFrequency (%)
_32861
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII582352
100.0%

Most frequent character per block

ValueCountFrequency (%)
o74339
12.8%
d53689
9.2%
t53475
9.2%
i53428
9.2%
n47410
8.1%
e46713
 
8.0%
c36231
 
6.2%
a35985
 
6.2%
_32861
 
5.6%
s30481
 
5.2%
Other values (12)117740
20.2%

title
Categorical

HIGH CARDINALITY

Distinct21264
Distinct (%)50.0%
Missing14
Missing (%)< 0.1%
Memory size332.4 KiB
Debt Consolidation
 
2259
Debt Consolidation Loan
 
1760
Personal Loan
 
708
Consolidation
 
547
debt consolidation
 
532
Other values (21259)
36716 

Length

Max length80
Median length16
Mean length17.34558581
Min length2

Characters and Unicode

Total characters737569
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19113 ?
Unique (%)44.9%

Sample

1st rowComputer
2nd rowbike
3rd rowreal estate business
4th rowpersonel
5th rowPersonal
ValueCountFrequency (%)
Debt Consolidation2259
 
5.3%
Debt Consolidation Loan1760
 
4.1%
Personal Loan708
 
1.7%
Consolidation547
 
1.3%
debt consolidation532
 
1.3%
Home Improvement373
 
0.9%
Credit Card Consolidation370
 
0.9%
Debt consolidation347
 
0.8%
Small Business Loan333
 
0.8%
Personal330
 
0.8%
Other values (21254)34963
82.2%
2021-03-24T09:36:00.824999image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan11503
 
10.2%
debt9723
 
8.6%
consolidation8994
 
7.9%
credit4952
 
4.4%
card3557
 
3.1%
personal2221
 
2.0%
home2025
 
1.8%
pay1473
 
1.3%
off1381
 
1.2%
to1286
 
1.1%
Other values (9389)66119
58.4%

Most occurring characters

ValueCountFrequency (%)
71985
 
9.8%
o70249
 
9.5%
n59793
 
8.1%
e59393
 
8.1%
a53923
 
7.3%
i47247
 
6.4%
t45984
 
6.2%
d33017
 
4.5%
r31780
 
4.3%
s31001
 
4.2%
Other values (98)233197
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter563571
76.4%
Uppercase Letter89179
 
12.1%
Space Separator71985
 
9.8%
Decimal Number6403
 
0.9%
Other Punctuation4910
 
0.7%
Dash Punctuation870
 
0.1%
Connector Punctuation220
 
< 0.1%
Close Punctuation112
 
< 0.1%
Currency Symbol104
 
< 0.1%
Math Symbol103
 
< 0.1%
Other values (5)112
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C19640
22.0%
L10896
12.2%
D9704
10.9%
P6064
 
6.8%
R4044
 
4.5%
S3521
 
3.9%
M3502
 
3.9%
B3341
 
3.7%
H3187
 
3.6%
E3142
 
3.5%
Other values (18)22138
24.8%
ValueCountFrequency (%)
o70249
12.5%
n59793
10.6%
e59393
10.5%
a53923
9.6%
i47247
8.4%
t45984
8.2%
d33017
 
5.9%
r31780
 
5.6%
s31001
 
5.5%
l28340
 
5.0%
Other values (18)102844
18.2%
ValueCountFrequency (%)
!1254
25.5%
'1068
21.8%
.813
16.6%
/589
12.0%
,481
 
9.8%
&353
 
7.2%
%105
 
2.1%
:67
 
1.4%
"61
 
1.2%
?27
 
0.5%
Other values (5)92
 
1.9%
ValueCountFrequency (%)
01822
28.5%
11761
27.5%
21188
18.6%
3316
 
4.9%
5275
 
4.3%
9264
 
4.1%
4234
 
3.7%
6195
 
3.0%
8175
 
2.7%
7173
 
2.7%
ValueCountFrequency (%)
€4
21.1%
4
21.1%
—4
21.1%
2
10.5%
™2
10.5%
–1
 
5.3%
‚1
 
5.3%
…1
 
5.3%
ValueCountFrequency (%)
+63
61.2%
=19
 
18.4%
<9
 
8.7%
>8
 
7.8%
~3
 
2.9%
|1
 
1.0%
ValueCountFrequency (%)
^1
33.3%
´1
33.3%
`1
33.3%
ValueCountFrequency (%)
(84
96.6%
[3
 
3.4%
ValueCountFrequency (%)
)108
96.4%
]4
 
3.6%
ValueCountFrequency (%)
71985
100.0%
ValueCountFrequency (%)
-870
100.0%
ValueCountFrequency (%)
_220
100.0%
ValueCountFrequency (%)
$104
100.0%
ValueCountFrequency (%)
³1
100.0%
ValueCountFrequency (%)
¦2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin652750
88.5%
Common84819
 
11.5%

Most frequent character per script

ValueCountFrequency (%)
o70249
 
10.8%
n59793
 
9.2%
e59393
 
9.1%
a53923
 
8.3%
i47247
 
7.2%
t45984
 
7.0%
d33017
 
5.1%
r31780
 
4.9%
s31001
 
4.7%
l28340
 
4.3%
Other values (46)192023
29.4%
ValueCountFrequency (%)
71985
84.9%
01822
 
2.1%
11761
 
2.1%
!1254
 
1.5%
21188
 
1.4%
'1068
 
1.3%
-870
 
1.0%
.813
 
1.0%
/589
 
0.7%
,481
 
0.6%
Other values (42)2988
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII737537
> 99.9%
None32
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
71985
 
9.8%
o70249
 
9.5%
n59793
 
8.1%
e59393
 
8.1%
a53923
 
7.3%
i47247
 
6.4%
t45984
 
6.2%
d33017
 
4.5%
r31780
 
4.3%
s31001
 
4.2%
Other values (84)233165
31.6%
ValueCountFrequency (%)
â4
12.5%
€4
12.5%
î4
12.5%
4
12.5%
—4
12.5%
Ã2
6.2%
™2
6.2%
¦2
6.2%
–1
 
3.1%
‚1
 
3.1%
Other values (4)4
12.5%

zip_code
Categorical

HIGH CARDINALITY

Distinct837
Distinct (%)2.0%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
100xx
 
649
945xx
 
559
606xx
 
548
112xx
 
538
070xx
 
503
Other values (832)
39738 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters212675
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)0.1%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row972xx
ValueCountFrequency (%)
100xx649
 
1.5%
945xx559
 
1.3%
606xx548
 
1.3%
112xx538
 
1.3%
070xx503
 
1.2%
900xx478
 
1.1%
300xx436
 
1.0%
021xx416
 
1.0%
750xx392
 
0.9%
926xx387
 
0.9%
Other values (827)37629
88.5%
2021-03-24T09:36:01.034739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100xx649
 
1.5%
945xx559
 
1.3%
606xx548
 
1.3%
112xx538
 
1.3%
070xx503
 
1.2%
900xx478
 
1.1%
300xx436
 
1.0%
021xx416
 
1.0%
750xx392
 
0.9%
926xx387
 
0.9%
Other values (827)37629
88.5%

Most occurring characters

ValueCountFrequency (%)
x85070
40.0%
021264
 
10.0%
116676
 
7.8%
214431
 
6.8%
913409
 
6.3%
313348
 
6.3%
710980
 
5.2%
49793
 
4.6%
59663
 
4.5%
89322
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127605
60.0%
Lowercase Letter85070
40.0%

Most frequent character per category

ValueCountFrequency (%)
021264
16.7%
116676
13.1%
214431
11.3%
913409
10.5%
313348
10.5%
710980
8.6%
49793
7.7%
59663
7.6%
89322
7.3%
68719
6.8%
ValueCountFrequency (%)
x85070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127605
60.0%
Latin85070
40.0%

Most frequent character per script

ValueCountFrequency (%)
021264
16.7%
116676
13.1%
214431
11.3%
913409
10.5%
313348
10.5%
710980
8.6%
49793
7.7%
59663
7.6%
89322
7.3%
68719
6.8%
ValueCountFrequency (%)
x85070
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII212675
100.0%

Most frequent character per block

ValueCountFrequency (%)
x85070
40.0%
021264
 
10.0%
116676
 
7.8%
214431
 
6.8%
913409
 
6.3%
313348
 
6.3%
710980
 
5.2%
49793
 
4.6%
59663
 
4.5%
89322
 
4.4%

addr_state
Categorical

HIGH CORRELATION

Distinct50
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
CA
7429 
NY
4065 
FL
3104 
TX
2915 
NJ
 
1988
Other values (45)
23034 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters85070
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR
ValueCountFrequency (%)
CA7429
17.5%
NY4065
 
9.6%
FL3104
 
7.3%
TX2915
 
6.9%
NJ1988
 
4.7%
IL1672
 
3.9%
PA1651
 
3.9%
GA1503
 
3.5%
VA1487
 
3.5%
MA1438
 
3.4%
Other values (40)15283
35.9%
2021-03-24T09:36:01.222098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca7429
17.5%
ny4065
 
9.6%
fl3104
 
7.3%
tx2915
 
6.9%
nj1988
 
4.7%
il1672
 
3.9%
pa1651
 
3.9%
ga1503
 
3.5%
va1487
 
3.5%
ma1438
 
3.4%
Other values (40)15283
35.9%

Most occurring characters

ValueCountFrequency (%)
A16633
19.6%
C10645
12.5%
N8517
10.0%
L5721
 
6.7%
M5106
 
6.0%
Y4511
 
5.3%
T4194
 
4.9%
O3736
 
4.4%
I3413
 
4.0%
F3104
 
3.6%
Other values (14)19490
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter85070
100.0%

Most frequent character per category

ValueCountFrequency (%)
A16633
19.6%
C10645
12.5%
N8517
10.0%
L5721
 
6.7%
M5106
 
6.0%
Y4511
 
5.3%
T4194
 
4.9%
O3736
 
4.4%
I3413
 
4.0%
F3104
 
3.6%
Other values (14)19490
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin85070
100.0%

Most frequent character per script

ValueCountFrequency (%)
A16633
19.6%
C10645
12.5%
N8517
10.0%
L5721
 
6.7%
M5106
 
6.0%
Y4511
 
5.3%
T4194
 
4.9%
O3736
 
4.4%
I3413
 
4.0%
F3104
 
3.6%
Other values (14)19490
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII85070
100.0%

Most frequent character per block

ValueCountFrequency (%)
A16633
19.6%
C10645
12.5%
N8517
10.0%
L5721
 
6.7%
M5106
 
6.0%
Y4511
 
5.3%
T4194
 
4.9%
O3736
 
4.4%
I3413
 
4.0%
F3104
 
3.6%
Other values (14)19490
22.9%

dti
Real number (ℝ≥0)

Distinct2894
Distinct (%)6.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13.37304314
Minimum0
Maximum29.99
Zeros206
Zeros (%)0.5%
Memory size332.4 KiB
2021-03-24T09:36:01.304981image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.1
Q18.2
median13.47
Q318.68
95-th percentile23.92
Maximum29.99
Range29.99
Interquartile range (IQR)10.48

Descriptive statistics

Standard deviation6.726314902
Coefficient of variation (CV)0.5029756377
Kurtosis-0.8517436241
Mean13.37304314
Median Absolute Deviation (MAD)5.24
Skewness-0.02992193534
Sum568822.39
Variance45.24331216
MonotocityNot monotonic
2021-03-24T09:36:01.397017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0206
 
0.5%
1254
 
0.1%
1046
 
0.1%
1846
 
0.1%
19.245
 
0.1%
13.243
 
0.1%
13.541
 
0.1%
16.841
 
0.1%
12.4840
 
0.1%
1538
 
0.1%
Other values (2884)41935
98.6%
ValueCountFrequency (%)
0206
0.5%
0.013
 
< 0.1%
0.025
 
< 0.1%
0.032
 
< 0.1%
0.043
 
< 0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.961
 
< 0.1%
29.952
< 0.1%
29.933
< 0.1%
29.922
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing30
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.152449066
Minimum0
Maximum13
Zeros37771
Zeros (%)88.8%
Memory size332.4 KiB
2021-03-24T09:36:01.477688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.512406485
Coefficient of variation (CV)3.361165131
Kurtosis51.07281075
Mean0.152449066
Median Absolute Deviation (MAD)0
Skewness5.433362395
Sum6480
Variance0.2625604059
MonotocityNot monotonic
2021-03-24T09:36:01.552882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
037771
88.8%
13595
 
8.5%
2771
 
1.8%
3244
 
0.6%
472
 
0.2%
527
 
0.1%
613
 
< 0.1%
76
 
< 0.1%
83
 
< 0.1%
112
 
< 0.1%
Other values (2)2
 
< 0.1%
(Missing)30
 
0.1%
ValueCountFrequency (%)
037771
88.8%
13595
 
8.5%
2771
 
1.8%
3244
 
0.6%
472
 
0.2%
ValueCountFrequency (%)
131
 
< 0.1%
112
 
< 0.1%
91
 
< 0.1%
83
< 0.1%
76
< 0.1%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct530
Distinct (%)1.2%
Missing30
Missing (%)0.1%
Memory size332.4 KiB
Oct-1999
 
393
Nov-1998
 
390
Oct-2000
 
370
Dec-1998
 
366
Dec-1997
 
348
Other values (525)
40639 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters340048
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.1%

Sample

1st rowJan-1985
2nd rowApr-1999
3rd rowNov-2001
4th rowFeb-1996
5th rowJan-1996
ValueCountFrequency (%)
Oct-1999393
 
0.9%
Nov-1998390
 
0.9%
Oct-2000370
 
0.9%
Dec-1998366
 
0.9%
Dec-1997348
 
0.8%
Nov-2000340
 
0.8%
Nov-1999337
 
0.8%
Oct-1998334
 
0.8%
Sep-2000325
 
0.8%
Nov-1997319
 
0.7%
Other values (520)38984
91.6%
2021-03-24T09:36:01.750492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oct-1999393
 
0.9%
nov-1998390
 
0.9%
oct-2000370
 
0.9%
dec-1998366
 
0.9%
dec-1997348
 
0.8%
nov-2000340
 
0.8%
nov-1999337
 
0.8%
oct-1998334
 
0.8%
sep-2000325
 
0.8%
nov-1997319
 
0.8%
Other values (520)38984
91.7%

Most occurring characters

ValueCountFrequency (%)
951514
15.1%
-42506
 
12.5%
036660
 
10.8%
130534
 
9.0%
219450
 
5.7%
e11233
 
3.3%
J10093
 
3.0%
u9950
 
2.9%
a9771
 
2.9%
89027
 
2.7%
Other values (23)109310
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number170024
50.0%
Lowercase Letter85012
25.0%
Uppercase Letter42506
 
12.5%
Dash Punctuation42506
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e11233
13.2%
u9950
11.7%
a9771
11.5%
c8713
10.2%
n6821
8.0%
p6799
8.0%
r5951
7.0%
t4396
 
5.2%
o4196
 
4.9%
v4196
 
4.9%
Other values (4)12986
15.3%
ValueCountFrequency (%)
951514
30.3%
036660
21.6%
130534
18.0%
219450
 
11.4%
89027
 
5.3%
75164
 
3.0%
44572
 
2.7%
64557
 
2.7%
54519
 
2.7%
34027
 
2.4%
ValueCountFrequency (%)
J10093
23.7%
A6490
15.3%
M6098
14.3%
O4396
10.3%
D4317
10.2%
N4196
9.9%
S3839
 
9.0%
F3077
 
7.2%
ValueCountFrequency (%)
-42506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common212530
62.5%
Latin127518
37.5%

Most frequent character per script

ValueCountFrequency (%)
e11233
 
8.8%
J10093
 
7.9%
u9950
 
7.8%
a9771
 
7.7%
c8713
 
6.8%
n6821
 
5.3%
p6799
 
5.3%
A6490
 
5.1%
M6098
 
4.8%
r5951
 
4.7%
Other values (12)45599
35.8%
ValueCountFrequency (%)
951514
24.2%
-42506
20.0%
036660
17.2%
130534
14.4%
219450
 
9.2%
89027
 
4.2%
75164
 
2.4%
44572
 
2.2%
64557
 
2.1%
54519
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII340048
100.0%

Most frequent character per block

ValueCountFrequency (%)
951514
15.1%
-42506
 
12.5%
036660
 
10.8%
130534
 
9.0%
219450
 
5.7%
e11233
 
3.3%
J10093
 
3.0%
u9950
 
2.9%
a9771
 
2.9%
89027
 
2.7%
Other values (23)109310
32.1%

fico_range_low
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean713.052545
Minimum610
Maximum825
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:36:01.833234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile665
Q1685
median710
Q3740
95-th percentile780
Maximum825
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation36.18843854
Coefficient of variation (CV)0.05075143311
Kurtosis-0.4963339445
Mean713.052545
Median Absolute Deviation (MAD)25
Skewness0.4648712594
Sum30329690
Variance1309.603084
MonotocityNot monotonic
2021-03-24T09:36:01.917961image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6852310
 
5.4%
7002267
 
5.3%
6802228
 
5.2%
6952202
 
5.2%
6902196
 
5.2%
6751994
 
4.7%
7051970
 
4.6%
7201949
 
4.6%
7251891
 
4.4%
7151891
 
4.4%
Other values (34)21637
50.9%
ValueCountFrequency (%)
6102
 
< 0.1%
6151
 
< 0.1%
6201
 
< 0.1%
6252
 
< 0.1%
6306
< 0.1%
ValueCountFrequency (%)
8253
 
< 0.1%
82019
 
< 0.1%
81528
 
0.1%
810125
0.3%
805193
0.5%

fico_range_high
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean717.052545
Minimum614
Maximum829
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:36:02.006134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum614
5-th percentile669
Q1689
median714
Q3744
95-th percentile784
Maximum829
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation36.18843854
Coefficient of variation (CV)0.05046832173
Kurtosis-0.4963339445
Mean717.052545
Median Absolute Deviation (MAD)25
Skewness0.4648712594
Sum30499830
Variance1309.603084
MonotocityNot monotonic
2021-03-24T09:36:02.087298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6892310
 
5.4%
7042267
 
5.3%
6842228
 
5.2%
6992202
 
5.2%
6942196
 
5.2%
6791994
 
4.7%
7091970
 
4.6%
7241949
 
4.6%
7191891
 
4.4%
7291891
 
4.4%
Other values (34)21637
50.9%
ValueCountFrequency (%)
6142
 
< 0.1%
6191
 
< 0.1%
6241
 
< 0.1%
6292
 
< 0.1%
6346
< 0.1%
ValueCountFrequency (%)
8293
 
< 0.1%
82419
 
< 0.1%
81928
 
0.1%
814125
0.3%
809193
0.5%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.1%
Missing30
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.081423799
Minimum0
Maximum33
Zeros19657
Zeros (%)46.2%
Memory size332.4 KiB
2021-03-24T09:36:02.166244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.527454835
Coefficient of variation (CV)1.412447957
Kurtosis30.96220493
Mean1.081423799
Median Absolute Deviation (MAD)1
Skewness3.453516564
Sum45967
Variance2.333118274
MonotocityNot monotonic
2021-03-24T09:36:02.242558image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
019657
46.2%
111247
26.4%
25987
 
14.1%
33182
 
7.5%
41056
 
2.5%
5596
 
1.4%
6339
 
0.8%
7182
 
0.4%
8115
 
0.3%
950
 
0.1%
Other values (18)95
 
0.2%
(Missing)30
 
0.1%
ValueCountFrequency (%)
019657
46.2%
111247
26.4%
25987
 
14.1%
33182
 
7.5%
41056
 
2.5%
ValueCountFrequency (%)
331
< 0.1%
321
< 0.1%
311
< 0.1%
281
< 0.1%
271
< 0.1%

mths_since_last_delinq
Real number (ℝ≥0)

ZEROS

Distinct95
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.85005642
Minimum0
Maximum120
Zeros27748
Zeros (%)65.2%
Memory size332.4 KiB
2021-03-24T09:36:02.332163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q321
95-th percentile64
Maximum120
Range120
Interquartile range (IQR)21

Descriptive statistics

Standard deviation21.66292728
Coefficient of variation (CV)1.685823514
Kurtosis1.356389979
Mean12.85005642
Median Absolute Deviation (MAD)0
Skewness1.590947228
Sum546590
Variance469.2824182
MonotocityNot monotonic
2021-03-24T09:36:02.432284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027748
65.2%
30270
 
0.6%
23266
 
0.6%
19266
 
0.6%
15263
 
0.6%
24262
 
0.6%
18252
 
0.6%
38251
 
0.6%
20249
 
0.6%
22248
 
0.6%
Other values (85)12461
29.3%
ValueCountFrequency (%)
027748
65.2%
133
 
0.1%
2115
 
0.3%
3157
 
0.4%
4164
 
0.4%
ValueCountFrequency (%)
1201
< 0.1%
1151
< 0.1%
1071
< 0.1%
1061
< 0.1%
1032
< 0.1%

mths_since_last_record
Real number (ℝ≥0)

MISSING
ZEROS

Distinct113
Distinct (%)3.1%
Missing38885
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean59.17556834
Minimum0
Maximum129
Zeros1275
Zeros (%)3.0%
Memory size332.4 KiB
2021-03-24T09:36:02.525631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median85
Q3101
95-th percentile115
Maximum129
Range129
Interquartile range (IQR)101

Descriptive statistics

Standard deviation47.1453958
Coefficient of variation (CV)0.7967037263
Kurtosis-1.694391517
Mean59.17556834
Median Absolute Deviation (MAD)28
Skewness-0.2834457395
Sum216050
Variance2222.688345
MonotocityNot monotonic
2021-03-24T09:36:02.623715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01275
 
3.0%
8970
 
0.2%
11368
 
0.2%
10466
 
0.2%
8662
 
0.1%
8762
 
0.1%
9461
 
0.1%
10060
 
0.1%
11160
 
0.1%
8859
 
0.1%
Other values (103)1808
 
4.3%
(Missing)38885
91.4%
ValueCountFrequency (%)
01275
3.0%
51
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
112
 
< 0.1%
ValueCountFrequency (%)
1291
 
< 0.1%
1201
 
< 0.1%
11910
 
< 0.1%
11840
0.1%
11750
0.1%

open_acc
Real number (ℝ≥0)

Distinct44
Distinct (%)0.1%
Missing30
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9.343951442
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:36:02.716938image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q312
95-th percentile18
Maximum47
Range46
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.496273876
Coefficient of variation (CV)0.4811961945
Kurtosis1.935047655
Mean9.343951442
Median Absolute Deviation (MAD)3
Skewness1.042024818
Sum397174
Variance20.21647877
MonotocityNot monotonic
2021-03-24T09:36:02.808449image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
74252
10.0%
84176
9.8%
64172
9.8%
93922
9.2%
103386
 
8.0%
53368
 
7.9%
112944
 
6.9%
42508
 
5.9%
122398
 
5.6%
132060
 
4.8%
Other values (34)9320
21.9%
ValueCountFrequency (%)
139
 
0.1%
2692
 
1.6%
31608
3.8%
42508
5.9%
53368
7.9%
ValueCountFrequency (%)
471
< 0.1%
461
< 0.1%
441
< 0.1%
421
< 0.1%
411
< 0.1%

pub_rec
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing30
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.05815649555
Minimum0
Maximum5
Zeros40130
Zeros (%)94.3%
Memory size332.4 KiB
2021-03-24T09:36:03.464403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2457131407
Coefficient of variation (CV)4.225033478
Kurtosis26.83502204
Mean0.05815649555
Median Absolute Deviation (MAD)0
Skewness4.605515995
Sum2472
Variance0.06037494749
MonotocityNot monotonic
2021-03-24T09:36:03.533426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
040130
94.3%
12298
 
5.4%
264
 
0.2%
311
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
(Missing)30
 
0.1%
ValueCountFrequency (%)
040130
94.3%
12298
 
5.4%
264
 
0.2%
311
 
< 0.1%
42
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
42
 
< 0.1%
311
 
< 0.1%
264
 
0.2%
12298
5.4%

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct22709
Distinct (%)53.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14297.86091
Minimum0
Maximum1207359
Zeros1119
Zeros (%)2.6%
Memory size332.4 KiB
2021-03-24T09:36:03.621348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile295.4
Q13635
median8821
Q317251
95-th percentile44543.9
Maximum1207359
Range1207359
Interquartile range (IQR)13616

Descriptive statistics

Standard deviation22018.44101
Coefficient of variation (CV)1.53998148
Kurtosis346.2882917
Mean14297.86091
Median Absolute Deviation (MAD)6092
Skewness11.01229081
Sum608159514
Variance484811744.5
MonotocityNot monotonic
2021-03-24T09:36:03.724672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01119
 
2.6%
25514
 
< 0.1%
29814
 
< 0.1%
113
 
< 0.1%
68212
 
< 0.1%
5210
 
< 0.1%
610
 
< 0.1%
40010
 
< 0.1%
3910
 
< 0.1%
17639
 
< 0.1%
Other values (22699)41314
97.1%
ValueCountFrequency (%)
01119
2.6%
113
 
< 0.1%
26
 
< 0.1%
37
 
< 0.1%
43
 
< 0.1%
ValueCountFrequency (%)
12073591
< 0.1%
9520131
< 0.1%
6025191
< 0.1%
5089611
< 0.1%
4875891
< 0.1%

revol_util
Categorical

HIGH CARDINALITY

Distinct1119
Distinct (%)2.6%
Missing91
Missing (%)0.2%
Memory size332.4 KiB
0%
 
1070
40.7%
 
65
0.2%
 
64
63%
 
63
66.6%
 
62
Other values (1114)
41121 

Length

Max length6
Median length5
Mean length4.646530805
Min length2

Characters and Unicode

Total characters197222
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)0.3%

Sample

1st row83.7%
2nd row9.4%
3rd row98.5%
4th row21%
5th row53.9%
ValueCountFrequency (%)
0%1070
 
2.5%
40.7%65
 
0.2%
0.2%64
 
0.2%
63%63
 
0.1%
66.6%62
 
0.1%
0.1%61
 
0.1%
70.4%61
 
0.1%
37.6%60
 
0.1%
64.6%60
 
0.1%
35.3%59
 
0.1%
Other values (1109)40820
96.0%
(Missing)91
 
0.2%
2021-03-24T09:36:03.930025image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01070
 
2.5%
40.765
 
0.2%
0.264
 
0.2%
6363
 
0.1%
66.662
 
0.1%
0.161
 
0.1%
70.461
 
0.1%
64.660
 
0.1%
37.660
 
0.1%
66.759
 
0.1%
Other values (1109)40820
96.2%

Most occurring characters

ValueCountFrequency (%)
%42445
21.5%
.37253
18.9%
412918
 
6.5%
512907
 
6.5%
612837
 
6.5%
712831
 
6.5%
312671
 
6.4%
212329
 
6.3%
812268
 
6.2%
111830
 
6.0%
Other values (2)16933
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number117524
59.6%
Other Punctuation79698
40.4%

Most frequent character per category

ValueCountFrequency (%)
412918
11.0%
512907
11.0%
612837
10.9%
712831
10.9%
312671
10.8%
212329
10.5%
812268
10.4%
111830
10.1%
911612
9.9%
05321
4.5%
ValueCountFrequency (%)
%42445
53.3%
.37253
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common197222
100.0%

Most frequent character per script

ValueCountFrequency (%)
%42445
21.5%
.37253
18.9%
412918
 
6.5%
512907
 
6.5%
612837
 
6.5%
712831
 
6.5%
312671
 
6.4%
212329
 
6.3%
812268
 
6.2%
111830
 
6.0%
Other values (2)16933
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII197222
100.0%

Most frequent character per block

ValueCountFrequency (%)
%42445
21.5%
.37253
18.9%
412918
 
6.5%
512907
 
6.5%
612837
 
6.5%
712831
 
6.5%
312671
 
6.4%
212329
 
6.3%
812268
 
6.2%
111830
 
6.0%
Other values (2)16933
 
8.6%

total_acc
Real number (ℝ≥0)

Distinct83
Distinct (%)0.2%
Missing30
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean22.12440597
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:36:04.021655image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q113
median20
Q329
95-th percentile44
Maximum90
Range89
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.59281134
Coefficient of variation (CV)0.5239829424
Kurtosis0.6588546244
Mean22.12440597
Median Absolute Deviation (MAD)8
Skewness0.8223755587
Sum940420
Variance134.3932747
MonotocityNot monotonic
2021-03-24T09:36:04.112754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151552
 
3.6%
161547
 
3.6%
171543
 
3.6%
141531
 
3.6%
201504
 
3.5%
181493
 
3.5%
211483
 
3.5%
131480
 
3.5%
121416
 
3.3%
191404
 
3.3%
Other values (73)27553
64.8%
ValueCountFrequency (%)
121
 
< 0.1%
241
 
0.1%
3238
 
0.6%
4486
1.1%
5622
1.5%
ValueCountFrequency (%)
901
< 0.1%
871
< 0.1%
811
< 0.1%
801
< 0.1%
792
< 0.1%

initial_list_status
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size83.2 KiB
False
42535 
(Missing)
 
1
ValueCountFrequency (%)
False42535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:04.172555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

out_prncp
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
0.0
42535 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127605
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:04.291408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:04.339227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042535
100.0%

Most occurring characters

ValueCountFrequency (%)
085070
66.7%
.42535
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85070
66.7%
Other Punctuation42535
33.3%

Most frequent character per category

ValueCountFrequency (%)
085070
100.0%
ValueCountFrequency (%)
.42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127605
100.0%

Most frequent character per script

ValueCountFrequency (%)
085070
66.7%
.42535
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII127605
100.0%

Most frequent character per block

ValueCountFrequency (%)
085070
66.7%
.42535
33.3%

out_prncp_inv
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
0.0
42535 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127605
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:04.455622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:04.503594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042535
100.0%

Most occurring characters

ValueCountFrequency (%)
085070
66.7%
.42535
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85070
66.7%
Other Punctuation42535
33.3%

Most frequent character per category

ValueCountFrequency (%)
085070
100.0%
ValueCountFrequency (%)
.42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127605
100.0%

Most frequent character per script

ValueCountFrequency (%)
085070
66.7%
.42535
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII127605
100.0%

Most frequent character per block

ValueCountFrequency (%)
085070
66.7%
.42535
33.3%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42366
Distinct (%)99.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12020.18964
Minimum0
Maximum58886.47343
Zeros19
Zeros (%)< 0.1%
Memory size332.4 KiB
2021-03-24T09:36:04.568495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1806.248222
Q15465.685189
median9682.251696
Q316427.85007
95-th percentile30211.27777
Maximum58886.47343
Range58886.47343
Interquartile range (IQR)10962.16488

Descriptive statistics

Standard deviation9094.685888
Coefficient of variation (CV)0.7566175041
Kurtosis2.175341593
Mean12020.18964
Median Absolute Deviation (MAD)4994.958304
Skewness1.379195192
Sum511278766.5
Variance82713311.4
MonotocityNot monotonic
2021-03-24T09:36:04.660109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019
 
< 0.1%
12029.456
 
< 0.1%
906
 
< 0.1%
1804
 
< 0.1%
453
 
< 0.1%
11804.689323
 
< 0.1%
11553.895943
 
< 0.1%
26687.233
 
< 0.1%
11437.691453
 
< 0.1%
11955.399213
 
< 0.1%
Other values (42356)42482
99.9%
ValueCountFrequency (%)
019
< 0.1%
0.751
 
< 0.1%
0.81
 
< 0.1%
33.971
 
< 0.1%
35.91
 
< 0.1%
ValueCountFrequency (%)
58886.473431
< 0.1%
58563.679931
< 0.1%
58480.139921
< 0.1%
58133.31991
< 0.1%
58090.952071
< 0.1%

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40102
Distinct (%)94.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11312.72938
Minimum0
Maximum58563.68
Zeros297
Zeros (%)0.7%
Memory size332.4 KiB
2021-03-24T09:36:04.767495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1161.239
Q14793.115
median8956.08
Q315543.875
95-th percentile29629.481
Maximum58563.68
Range58563.68
Interquartile range (IQR)10750.76

Descriptive statistics

Standard deviation9038.506549
Coefficient of variation (CV)0.7989678042
Kurtosis2.220726227
Mean11312.72938
Median Absolute Deviation (MAD)4981.82
Skewness1.393032244
Sum481186944
Variance81694600.64
MonotocityNot monotonic
2021-03-24T09:36:04.859927image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0297
 
0.7%
6514.5216
 
< 0.1%
13148.1414
 
< 0.1%
5478.3914
 
< 0.1%
10956.7812
 
< 0.1%
6717.9512
 
< 0.1%
11196.5712
 
< 0.1%
7328.9211
 
< 0.1%
5557.0311
 
< 0.1%
13517.3611
 
< 0.1%
Other values (40092)42125
99.0%
ValueCountFrequency (%)
0297
0.7%
0.511
 
< 0.1%
0.541
 
< 0.1%
0.751
 
< 0.1%
0.81
 
< 0.1%
ValueCountFrequency (%)
58563.681
< 0.1%
58514.931
< 0.1%
58438.371
< 0.1%
58056.41
< 0.1%
57967.531
< 0.1%

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7691
Distinct (%)18.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean9675.675936
Minimum0
Maximum35000.02
Zeros86
Zeros (%)0.2%
Memory size332.4 KiB
2021-03-24T09:36:04.958705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1203.661
Q14400
median8000
Q313499.995
95-th percentile24999.99
Maximum35000.02
Range35000.02
Interquartile range (IQR)9099.995

Descriptive statistics

Standard deviation7105.750304
Coefficient of variation (CV)0.7343931681
Kurtosis1.143927598
Mean9675.675936
Median Absolute Deviation (MAD)4000
Skewness1.136695315
Sum411554875.9
Variance50491687.38
MonotocityNot monotonic
2021-03-24T09:36:05.050985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002432
 
5.7%
120001958
 
4.6%
50001861
 
4.4%
60001752
 
4.1%
150001529
 
3.6%
80001414
 
3.3%
200001200
 
2.8%
40001046
 
2.5%
3000975
 
2.3%
7000901
 
2.1%
Other values (7681)27467
64.6%
ValueCountFrequency (%)
086
0.2%
21.211
 
< 0.1%
21.931
 
< 0.1%
22.241
 
< 0.1%
22.51
 
< 0.1%
ValueCountFrequency (%)
35000.022
 
< 0.1%
35000.011
 
< 0.1%
35000418
1.0%
34999.997
 
< 0.1%
34999.981
 
< 0.1%

total_rec_int
Real number (ℝ≥0)

Distinct37462
Distinct (%)88.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2239.981444
Minimum0
Maximum23886.47
Zeros83
Zeros (%)0.2%
Memory size332.4 KiB
2021-03-24T09:36:05.159362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile183.961
Q1657.1
median1339.16
Q32803.09
95-th percentile7463.76
Maximum23886.47
Range23886.47
Interquartile range (IQR)2145.99

Descriptive statistics

Standard deviation2585.057393
Coefficient of variation (CV)1.154053039
Kurtosis10.0705644
Mean2239.981444
Median Absolute Deviation (MAD)858.74
Skewness2.70883429
Sum95277610.71
Variance6682521.725
MonotocityNot monotonic
2021-03-24T09:36:05.253085image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
083
 
0.2%
1196.5726
 
0.1%
514.5219
 
< 0.1%
1148.1417
 
< 0.1%
956.7817
 
< 0.1%
717.9517
 
< 0.1%
1784.2317
 
< 0.1%
478.3916
 
< 0.1%
1907.3514
 
< 0.1%
632.2113
 
< 0.1%
Other values (37452)42296
99.4%
ValueCountFrequency (%)
083
0.2%
3.541
 
< 0.1%
6.221
 
< 0.1%
6.271
 
< 0.1%
7.191
 
< 0.1%
ValueCountFrequency (%)
23886.471
< 0.1%
23563.681
< 0.1%
23480.141
< 0.1%
23090.951
< 0.1%
23084.931
< 0.1%

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct2299
Distinct (%)5.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.516889594
Minimum0
Maximum209
Zeros40145
Zeros (%)94.4%
Memory size332.4 KiB
2021-03-24T09:36:05.347646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.97978247
Maximum209
Range209
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.830063955
Coefficient of variation (CV)5.161920806
Kurtosis101.1595044
Mean1.516889594
Median Absolute Deviation (MAD)0
Skewness8.337377936
Sum64520.89888
Variance61.30990155
MonotocityNot monotonic
2021-03-24T09:36:05.441772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040145
94.4%
1569
 
0.2%
308
 
< 0.1%
453
 
< 0.1%
152
 
< 0.1%
602
 
< 0.1%
17.72
 
< 0.1%
452
 
< 0.1%
15.000000012
 
< 0.1%
152
 
< 0.1%
Other values (2289)2298
 
5.4%
ValueCountFrequency (%)
040145
94.4%
0.011
 
< 0.1%
0.06079975081
 
< 0.1%
0.07378710411
 
< 0.1%
0.10170456191
 
< 0.1%
ValueCountFrequency (%)
2091
< 0.1%
180.21
< 0.1%
170.76000041
< 0.1%
166.42971071
< 0.1%
165.691
< 0.1%

recoveries
Real number (ℝ≥0)

ZEROS

Distinct5058
Distinct (%)11.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean103.0154671
Minimum0
Maximum29623.35
Zeros36176
Zeros (%)85.0%
Memory size332.4 KiB
2021-03-24T09:36:05.537159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile392.912
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation735.7990861
Coefficient of variation (CV)7.142607871
Kurtosis386.5922346
Mean103.0154671
Median Absolute Deviation (MAD)0
Skewness16.57335707
Sum4381762.893
Variance541400.2951
MonotocityNot monotonic
2021-03-24T09:36:05.630382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036176
85.0%
0.314
 
< 0.1%
1.811
 
< 0.1%
0.9610
 
< 0.1%
1.210
 
< 0.1%
0.269
 
< 0.1%
0.99
 
< 0.1%
1.769
 
< 0.1%
6.39
 
< 0.1%
4.29
 
< 0.1%
Other values (5048)6269
 
14.7%
ValueCountFrequency (%)
036176
85.0%
0.017
 
< 0.1%
0.023
 
< 0.1%
0.034
 
< 0.1%
0.046
 
< 0.1%
ValueCountFrequency (%)
29623.351
< 0.1%
277501
< 0.1%
27009.471
< 0.1%
22943.371
< 0.1%
21811.731
< 0.1%

collection_recovery_fee
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2897
Distinct (%)6.8%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.38784299
Minimum0
Maximum7002.19
Zeros38207
Zeros (%)89.8%
Memory size332.4 KiB
2021-03-24T09:36:05.730193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.76
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation162.1778368
Coefficient of variation (CV)11.2718659
Kurtosis647.9221659
Mean14.38784299
Median Absolute Deviation (MAD)0
Skewness22.41007601
Sum611986.9015
Variance26301.65075
MonotocityNot monotonic
2021-03-24T09:36:05.825269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038207
89.8%
215
 
< 0.1%
1.211
 
< 0.1%
1.610
 
< 0.1%
1.219
 
< 0.1%
1.449
 
< 0.1%
1.89
 
< 0.1%
2.029
 
< 0.1%
1.559
 
< 0.1%
3.239
 
< 0.1%
Other values (2887)4238
 
10.0%
ValueCountFrequency (%)
038207
89.8%
0.04499999991
 
< 0.1%
0.0631
 
< 0.1%
0.07450000121
 
< 0.1%
0.11811
 
< 0.1%
ValueCountFrequency (%)
7002.191
< 0.1%
6972.591
< 0.1%
6543.041
< 0.1%
5774.81
< 0.1%
5602.721
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct112
Distinct (%)0.3%
Missing84
Missing (%)0.2%
Memory size332.4 KiB
Mar-2013
 
1070
Dec-2014
 
949
May-2013
 
943
Feb-2013
 
906
Mar-2012
 
893
Other values (107)
37691 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters339616
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJan-2015
2nd rowApr-2013
3rd rowJun-2014
4th rowJan-2015
5th rowJan-2017
ValueCountFrequency (%)
Mar-20131070
 
2.5%
Dec-2014949
 
2.2%
May-2013943
 
2.2%
Feb-2013906
 
2.1%
Mar-2012893
 
2.1%
Apr-2013890
 
2.1%
Aug-2012868
 
2.0%
Oct-2012853
 
2.0%
Jan-2014845
 
2.0%
Aug-2014836
 
2.0%
Other values (102)33399
78.5%
2021-03-24T09:36:06.019452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-20131070
 
2.5%
dec-2014949
 
2.2%
may-2013943
 
2.2%
feb-2013906
 
2.1%
mar-2012893
 
2.1%
apr-2013890
 
2.1%
aug-2012868
 
2.0%
oct-2012853
 
2.0%
jan-2014845
 
2.0%
aug-2014836
 
2.0%
Other values (102)33399
78.7%

Most occurring characters

ValueCountFrequency (%)
251803
15.3%
147025
13.8%
046047
13.6%
-42452
12.5%
e10783
 
3.2%
a10775
 
3.2%
u10502
 
3.1%
J10186
 
3.0%
39811
 
2.9%
49322
 
2.7%
Other values (23)90910
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169808
50.0%
Lowercase Letter84904
25.0%
Uppercase Letter42452
 
12.5%
Dash Punctuation42452
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e10783
12.7%
a10775
12.7%
u10502
12.4%
c7564
8.9%
r7494
8.8%
p6820
8.0%
n6573
7.7%
t3656
 
4.3%
l3613
 
4.3%
g3593
 
4.2%
Other values (4)13531
15.9%
ValueCountFrequency (%)
251803
30.5%
147025
27.7%
046047
27.1%
39811
 
5.8%
49322
 
5.5%
52511
 
1.5%
62083
 
1.2%
9838
 
0.5%
8335
 
0.2%
733
 
< 0.1%
ValueCountFrequency (%)
J10186
24.0%
M7498
17.7%
A7024
16.5%
D3908
 
9.2%
O3656
 
8.6%
F3486
 
8.2%
S3389
 
8.0%
N3305
 
7.8%
ValueCountFrequency (%)
-42452
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common212260
62.5%
Latin127356
37.5%

Most frequent character per script

ValueCountFrequency (%)
e10783
 
8.5%
a10775
 
8.5%
u10502
 
8.2%
J10186
 
8.0%
c7564
 
5.9%
M7498
 
5.9%
r7494
 
5.9%
A7024
 
5.5%
p6820
 
5.4%
n6573
 
5.2%
Other values (12)42137
33.1%
ValueCountFrequency (%)
251803
24.4%
147025
22.2%
046047
21.7%
-42452
20.0%
39811
 
4.6%
49322
 
4.4%
52511
 
1.2%
62083
 
1.0%
9838
 
0.4%
8335
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII339616
100.0%

Most frequent character per block

ValueCountFrequency (%)
251803
15.3%
147025
13.8%
046047
13.6%
-42452
12.5%
e10783
 
3.2%
a10775
 
3.2%
u10502
 
3.1%
J10186
 
3.0%
39811
 
2.9%
49322
 
2.7%
Other values (23)90910
26.8%

last_pymnt_amnt
Real number (ℝ≥0)

Distinct37260
Distinct (%)87.6%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2613.245652
Minimum0
Maximum36115.2
Zeros96
Zeros (%)0.2%
Memory size332.4 KiB
2021-03-24T09:36:06.111774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.18
Q1211.05
median528.36
Q33170.22
95-th percentile12018.095
Maximum36115.2
Range36115.2
Interquartile range (IQR)2959.17

Descriptive statistics

Standard deviation4385.066535
Coefficient of variation (CV)1.678015433
Kurtosis9.13496774
Mean2613.245652
Median Absolute Deviation (MAD)435.7
Skewness2.750044596
Sum111154403.8
Variance19228808.51
MonotocityNot monotonic
2021-03-24T09:36:06.212978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
096
 
0.2%
20019
 
< 0.1%
10017
 
< 0.1%
5017
 
< 0.1%
15013
 
< 0.1%
40012
 
< 0.1%
275.7411
 
< 0.1%
50011
 
< 0.1%
276.069
 
< 0.1%
2508
 
< 0.1%
Other values (37250)42322
99.5%
ValueCountFrequency (%)
096
0.2%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.061
 
< 0.1%
ValueCountFrequency (%)
36115.21
< 0.1%
35613.681
< 0.1%
35596.411
< 0.1%
35479.891
< 0.1%
35471.861
< 0.1%

next_pymnt_d
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct98
Distinct (%)3.6%
Missing39787
Missing (%)93.5%
Memory size332.4 KiB
Mar-2011
 
107
Apr-2011
 
101
Feb-2011
 
91
Jan-2011
 
79
May-2011
 
77
Other values (93)
2294 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters21992
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowJan-2016
2nd rowSep-2013
3rd rowFeb-2016
4th rowFeb-2014
5th rowMay-2014
ValueCountFrequency (%)
Mar-2011107
 
0.3%
Apr-2011101
 
0.2%
Feb-201191
 
0.2%
Jan-201179
 
0.2%
May-201177
 
0.2%
Dec-201071
 
0.2%
Jun-201166
 
0.2%
Sep-201163
 
0.1%
Aug-201157
 
0.1%
Nov-201055
 
0.1%
Other values (88)1982
 
4.7%
(Missing)39787
93.5%
2021-03-24T09:36:06.405942image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-2011107
 
3.9%
apr-2011101
 
3.7%
feb-201191
 
3.3%
jan-201179
 
2.9%
may-201177
 
2.8%
dec-201071
 
2.6%
jun-201166
 
2.4%
sep-201163
 
2.3%
aug-201157
 
2.1%
nov-201055
 
2.0%
Other values (88)1982
72.1%

Most occurring characters

ValueCountFrequency (%)
03682
16.7%
23221
14.6%
13151
14.3%
-2749
12.5%
a721
 
3.3%
e702
 
3.2%
J602
 
2.7%
u599
 
2.7%
c536
 
2.4%
r515
 
2.3%
Other values (22)5514
25.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10996
50.0%
Lowercase Letter5498
25.0%
Uppercase Letter2749
 
12.5%
Dash Punctuation2749
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
a721
13.1%
e702
12.8%
u599
10.9%
c536
9.7%
r515
9.4%
p453
8.2%
n418
7.6%
t251
 
4.6%
y234
 
4.3%
o228
 
4.1%
Other values (4)841
15.3%
ValueCountFrequency (%)
03682
33.5%
23221
29.3%
13151
28.7%
3372
 
3.4%
9312
 
2.8%
8106
 
1.0%
579
 
0.7%
463
 
0.6%
610
 
0.1%
ValueCountFrequency (%)
J602
21.9%
M501
18.2%
A465
16.9%
D285
10.4%
O251
9.1%
N228
 
8.3%
F212
 
7.7%
S205
 
7.5%
ValueCountFrequency (%)
-2749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13745
62.5%
Latin8247
37.5%

Most frequent character per script

ValueCountFrequency (%)
a721
 
8.7%
e702
 
8.5%
J602
 
7.3%
u599
 
7.3%
c536
 
6.5%
r515
 
6.2%
M501
 
6.1%
A465
 
5.6%
p453
 
5.5%
n418
 
5.1%
Other values (12)2735
33.2%
ValueCountFrequency (%)
03682
26.8%
23221
23.4%
13151
22.9%
-2749
20.0%
3372
 
2.7%
9312
 
2.3%
8106
 
0.8%
579
 
0.6%
463
 
0.5%
610
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII21992
100.0%

Most frequent character per block

ValueCountFrequency (%)
03682
16.7%
23221
14.6%
13151
14.3%
-2749
12.5%
a721
 
3.3%
e702
 
3.2%
J602
 
2.7%
u599
 
2.7%
c536
 
2.4%
r515
 
2.3%
Other values (22)5514
25.1%

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct133
Distinct (%)0.3%
Missing5
Missing (%)< 0.1%
Memory size332.4 KiB
Aug-2018
9248 
Oct-2016
4071 
Jul-2018
 
1233
May-2018
 
734
Feb-2017
 
709
Other values (128)
26536 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters340248
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAug-2018
2nd rowOct-2016
3rd rowJun-2017
4th rowApr-2016
5th rowApr-2018
ValueCountFrequency (%)
Aug-20189248
 
21.7%
Oct-20164071
 
9.6%
Jul-20181233
 
2.9%
May-2018734
 
1.7%
Feb-2017709
 
1.7%
Apr-2018600
 
1.4%
Mar-2018595
 
1.4%
Feb-2013564
 
1.3%
Jan-2018546
 
1.3%
Jun-2018531
 
1.2%
Other values (123)23700
55.7%
2021-03-24T09:36:06.578536image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aug-20189248
 
21.7%
oct-20164071
 
9.6%
jul-20181233
 
2.9%
may-2018734
 
1.7%
feb-2017709
 
1.7%
apr-2018600
 
1.4%
mar-2018595
 
1.4%
feb-2013564
 
1.3%
jan-2018546
 
1.3%
jun-2018531
 
1.2%
Other values (123)23700
55.7%

Most occurring characters

ValueCountFrequency (%)
245366
13.3%
143999
12.9%
043667
12.8%
-42531
12.5%
u16863
 
5.0%
814066
 
4.1%
A13810
 
4.1%
g11344
 
3.3%
c8212
 
2.4%
a7664
 
2.3%
Other values (23)92726
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number170124
50.0%
Lowercase Letter85062
25.0%
Uppercase Letter42531
 
12.5%
Dash Punctuation42531
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
u16863
19.8%
g11344
13.3%
c8212
9.7%
a7664
9.0%
e7436
8.7%
t5938
 
7.0%
r5402
 
6.4%
p4467
 
5.3%
n4428
 
5.2%
l3226
 
3.8%
Other values (4)10082
11.9%
ValueCountFrequency (%)
245366
26.7%
143999
25.9%
043667
25.7%
814066
 
8.3%
67405
 
4.4%
75040
 
3.0%
43972
 
2.3%
33513
 
2.1%
52853
 
1.7%
9243
 
0.1%
ValueCountFrequency (%)
A13810
32.5%
J7654
18.0%
O5938
14.0%
M5529
13.0%
F3161
 
7.4%
D2274
 
5.3%
N2164
 
5.1%
S2001
 
4.7%
ValueCountFrequency (%)
-42531
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common212655
62.5%
Latin127593
37.5%

Most frequent character per script

ValueCountFrequency (%)
u16863
13.2%
A13810
 
10.8%
g11344
 
8.9%
c8212
 
6.4%
a7664
 
6.0%
J7654
 
6.0%
e7436
 
5.8%
O5938
 
4.7%
t5938
 
4.7%
M5529
 
4.3%
Other values (12)37205
29.2%
ValueCountFrequency (%)
245366
21.3%
143999
20.7%
043667
20.5%
-42531
20.0%
814066
 
6.6%
67405
 
3.5%
75040
 
2.4%
43972
 
1.9%
33513
 
1.7%
52853
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII340248
100.0%

Most frequent character per block

ValueCountFrequency (%)
245366
13.3%
143999
12.9%
043667
12.8%
-42531
12.5%
u16863
 
5.0%
814066
 
4.1%
A13810
 
4.1%
g11344
 
3.3%
c8212
 
2.4%
a7664
 
2.3%
Other values (23)92726
27.3%

last_fico_range_high
Real number (ℝ≥0)

Distinct72
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean689.9225109
Minimum0
Maximum850
Zeros24
Zeros (%)0.1%
Memory size332.4 KiB
2021-03-24T09:36:06.662126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile534
Q1644
median699
Q3749
95-th percentile804
Maximum850
Range850
Interquartile range (IQR)105

Descriptive statistics

Standard deviation80.81809893
Coefficient of variation (CV)0.1171408349
Kurtosis2.471258434
Mean689.9225109
Median Absolute Deviation (MAD)50
Skewness-0.8229792113
Sum29345854
Variance6531.565115
MonotocityNot monotonic
2021-03-24T09:36:06.749003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7091256
 
3.0%
6941216
 
2.9%
7191204
 
2.8%
7241197
 
2.8%
7141175
 
2.8%
7041171
 
2.8%
6991149
 
2.7%
6841115
 
2.6%
6891077
 
2.5%
7341056
 
2.5%
Other values (62)30919
72.7%
ValueCountFrequency (%)
024
 
0.1%
499771
1.8%
504217
 
0.5%
509189
 
0.4%
514214
 
0.5%
ValueCountFrequency (%)
85010
 
< 0.1%
84432
 
0.1%
83946
 
0.1%
834142
0.3%
829190
0.4%

last_fico_range_low
Real number (ℝ≥0)

ZEROS

Distinct71
Distinct (%)0.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean676.9520395
Minimum0
Maximum845
Zeros795
Zeros (%)1.9%
Memory size332.4 KiB
2021-03-24T09:36:06.841678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile530
Q1640
median695
Q3745
95-th percentile800
Maximum845
Range845
Interquartile range (IQR)105

Descriptive statistics

Standard deviation119.6477519
Coefficient of variation (CV)0.1767447986
Kurtosis16.54509905
Mean676.9520395
Median Absolute Deviation (MAD)50
Skewness-3.368228991
Sum28794155
Variance14315.58452
MonotocityNot monotonic
2021-03-24T09:36:06.928674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7051256
 
3.0%
6901216
 
2.9%
7151204
 
2.8%
7201197
 
2.8%
7101175
 
2.8%
7001171
 
2.8%
6951149
 
2.7%
6801115
 
2.6%
6851077
 
2.5%
7301056
 
2.5%
Other values (61)30919
72.7%
ValueCountFrequency (%)
0795
1.9%
500217
 
0.5%
505189
 
0.4%
510214
 
0.5%
515214
 
0.5%
ValueCountFrequency (%)
84510
 
< 0.1%
84032
 
0.1%
83546
 
0.1%
830142
0.3%
825190
0.4%

collections_12_mths_ex_med
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing146
Missing (%)0.3%
Memory size332.4 KiB
0.0
42390 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127170
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042390
99.7%
(Missing)146
 
0.3%
2021-03-24T09:36:07.079696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:07.127412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042390
100.0%

Most occurring characters

ValueCountFrequency (%)
084780
66.7%
.42390
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84780
66.7%
Other Punctuation42390
33.3%

Most frequent character per category

ValueCountFrequency (%)
084780
100.0%
ValueCountFrequency (%)
.42390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127170
100.0%

Most frequent character per script

ValueCountFrequency (%)
084780
66.7%
.42390
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII127170
100.0%

Most frequent character per block

ValueCountFrequency (%)
084780
66.7%
.42390
33.3%

mths_since_last_major_derog
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

policy_code
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
1.0
42535 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127605
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.042535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:07.243813image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:07.291505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.042535
100.0%

Most occurring characters

ValueCountFrequency (%)
142535
33.3%
.42535
33.3%
042535
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85070
66.7%
Other Punctuation42535
33.3%

Most frequent character per category

ValueCountFrequency (%)
142535
50.0%
042535
50.0%
ValueCountFrequency (%)
.42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127605
100.0%

Most frequent character per script

ValueCountFrequency (%)
142535
33.3%
.42535
33.3%
042535
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII127605
100.0%

Most frequent character per block

ValueCountFrequency (%)
142535
33.3%
.42535
33.3%
042535
33.3%

application_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
Individual
42535 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters425350
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual
ValueCountFrequency (%)
Individual42535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:07.409205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:07.457618image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
individual42535
100.0%

Most occurring characters

ValueCountFrequency (%)
d85070
20.0%
i85070
20.0%
I42535
10.0%
n42535
10.0%
v42535
10.0%
u42535
10.0%
a42535
10.0%
l42535
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter382815
90.0%
Uppercase Letter42535
 
10.0%

Most frequent character per category

ValueCountFrequency (%)
d85070
22.2%
i85070
22.2%
n42535
11.1%
v42535
11.1%
u42535
11.1%
a42535
11.1%
l42535
11.1%
ValueCountFrequency (%)
I42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin425350
100.0%

Most frequent character per script

ValueCountFrequency (%)
d85070
20.0%
i85070
20.0%
I42535
10.0%
n42535
10.0%
v42535
10.0%
u42535
10.0%
a42535
10.0%
l42535
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII425350
100.0%

Most frequent character per block

ValueCountFrequency (%)
d85070
20.0%
i85070
20.0%
I42535
10.0%
n42535
10.0%
v42535
10.0%
u42535
10.0%
a42535
10.0%
l42535
10.0%

annual_inc_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

dti_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

verification_status_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

acc_now_delinq
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing30
Missing (%)0.1%
Memory size332.4 KiB
0.0
42502 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127518
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042502
99.9%
1.04
 
< 0.1%
(Missing)30
 
0.1%
2021-03-24T09:36:07.571602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:07.620332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042502
> 99.9%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
085008
66.7%
.42506
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85012
66.7%
Other Punctuation42506
33.3%

Most frequent character per category

ValueCountFrequency (%)
085008
> 99.9%
14
 
< 0.1%
ValueCountFrequency (%)
.42506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127518
100.0%

Most frequent character per script

ValueCountFrequency (%)
085008
66.7%
.42506
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII127518
100.0%

Most frequent character per block

ValueCountFrequency (%)
085008
66.7%
.42506
33.3%
14
 
< 0.1%

tot_coll_amt
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

tot_cur_bal
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

open_acc_6m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

open_act_il
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

open_il_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

open_il_24m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mths_since_rcnt_il
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

total_bal_il
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

il_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

open_rv_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

open_rv_24m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

max_bal_bc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

all_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

total_rev_hi_lim
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

inq_fi
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

total_cu_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

inq_last_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

acc_open_past_24mths
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

avg_cur_bal
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

bc_open_to_buy
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

bc_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

chargeoff_within_12_mths
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing146
Missing (%)0.3%
Memory size332.4 KiB
0.0
42390 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127170
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042390
99.7%
(Missing)146
 
0.3%
2021-03-24T09:36:07.742815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:07.790839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042390
100.0%

Most occurring characters

ValueCountFrequency (%)
084780
66.7%
.42390
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84780
66.7%
Other Punctuation42390
33.3%

Most frequent character per category

ValueCountFrequency (%)
084780
100.0%
ValueCountFrequency (%)
.42390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127170
100.0%

Most frequent character per script

ValueCountFrequency (%)
084780
66.7%
.42390
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII127170
100.0%

Most frequent character per block

ValueCountFrequency (%)
084780
66.7%
.42390
33.3%

delinq_amnt
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing30
Missing (%)0.1%
Memory size332.4 KiB
0.0
42504 
27.0
 
1
6053.0
 
1

Length

Max length6
Median length3
Mean length3.000094104
Min length3

Characters and Unicode

Total characters127522
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042504
99.9%
27.01
 
< 0.1%
6053.01
 
< 0.1%
(Missing)30
 
0.1%
2021-03-24T09:36:07.916012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:07.969991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042504
> 99.9%
27.01
 
< 0.1%
6053.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
085011
66.7%
.42506
33.3%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number85016
66.7%
Other Punctuation42506
33.3%

Most frequent character per category

ValueCountFrequency (%)
085011
> 99.9%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%
ValueCountFrequency (%)
.42506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127522
100.0%

Most frequent character per script

ValueCountFrequency (%)
085011
66.7%
.42506
33.3%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII127522
100.0%

Most frequent character per block

ValueCountFrequency (%)
085011
66.7%
.42506
33.3%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%

mo_sin_old_il_acct
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mo_sin_old_rev_tl_op
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mo_sin_rcnt_rev_tl_op
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mo_sin_rcnt_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mort_acc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mths_since_recent_bc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mths_since_recent_bc_dlq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mths_since_recent_inq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

mths_since_recent_revol_delinq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_accts_ever_120_pd
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_actv_bc_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_actv_rev_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_bc_sats
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_bc_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_il_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_op_rev_tl
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_rev_accts
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_rev_tl_bal_gt_0
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_sats
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_tl_120dpd_2m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_tl_30dpd
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_tl_90g_dpd_24m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

num_tl_op_past_12m
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

pct_tl_nvr_dlq
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

percent_bc_gt_75
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

pub_rec_bankruptcies
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing1366
Missing (%)3.2%
Memory size332.4 KiB
0.0
39316 
1.0
 
1846
2.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123510
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.039316
92.4%
1.01846
 
4.3%
2.08
 
< 0.1%
(Missing)1366
 
3.2%
2021-03-24T09:36:08.117962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:08.169213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.039316
95.5%
1.01846
 
4.5%
2.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
080486
65.2%
.41170
33.3%
11846
 
1.5%
28
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number82340
66.7%
Other Punctuation41170
33.3%

Most frequent character per category

ValueCountFrequency (%)
080486
97.7%
11846
 
2.2%
28
 
< 0.1%
ValueCountFrequency (%)
.41170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common123510
100.0%

Most frequent character per script

ValueCountFrequency (%)
080486
65.2%
.41170
33.3%
11846
 
1.5%
28
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII123510
100.0%

Most frequent character per block

ValueCountFrequency (%)
080486
65.2%
.41170
33.3%
11846
 
1.5%
28
 
< 0.1%

tax_liens
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing106
Missing (%)0.2%
Memory size332.4 KiB
0.0
42429 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127290
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042429
99.7%
1.01
 
< 0.1%
(Missing)106
 
0.2%
2021-03-24T09:36:08.295272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:08.344283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.042429
> 99.9%
1.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
084859
66.7%
.42430
33.3%
11
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84860
66.7%
Other Punctuation42430
33.3%

Most frequent character per category

ValueCountFrequency (%)
084859
> 99.9%
11
 
< 0.1%
ValueCountFrequency (%)
.42430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127290
100.0%

Most frequent character per script

ValueCountFrequency (%)
084859
66.7%
.42430
33.3%
11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII127290
100.0%

Most frequent character per block

ValueCountFrequency (%)
084859
66.7%
.42430
33.3%
11
 
< 0.1%

tot_hi_cred_lim
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

total_bal_ex_mort
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

total_bc_limit
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

total_il_high_credit_limit
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

revol_bal_joint
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_fico_range_low
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_fico_range_high
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_earliest_cr_line
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_inq_last_6mths
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_mort_acc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_open_acc
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_revol_util
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_open_act_il
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_num_rev_accts
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_chargeoff_within_12_mths
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_collections_12_mths_ex_med
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

sec_app_mths_since_last_major_derog
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_flag
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size83.2 KiB
False
42535 
(Missing)
 
1
ValueCountFrequency (%)
False42535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:08.373578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

hardship_type
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_reason
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_status
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

deferral_term
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_amount
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_start_date
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_end_date
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

payment_plan_start_date
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_length
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_dpd
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_loan_status
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

orig_projected_additional_accrued_interest
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_payoff_balance_amount
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

hardship_last_payment_amount
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing42536
Missing (%)100.0%
Memory size332.4 KiB

disbursement_method
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size332.4 KiB
Cash
42535 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters170140
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCash
2nd rowCash
3rd rowCash
4th rowCash
5th rowCash
ValueCountFrequency (%)
Cash42535
> 99.9%
(Missing)1
 
< 0.1%
2021-03-24T09:36:08.491596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:08.539827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cash42535
100.0%

Most occurring characters

ValueCountFrequency (%)
C42535
25.0%
a42535
25.0%
s42535
25.0%
h42535
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter127605
75.0%
Uppercase Letter42535
 
25.0%

Most frequent character per category

ValueCountFrequency (%)
a42535
33.3%
s42535
33.3%
h42535
33.3%
ValueCountFrequency (%)
C42535
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin170140
100.0%

Most frequent character per script

ValueCountFrequency (%)
C42535
25.0%
a42535
25.0%
s42535
25.0%
h42535
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII170140
100.0%

Most frequent character per block

ValueCountFrequency (%)
C42535
25.0%
a42535
25.0%
s42535
25.0%
h42535
25.0%

debt_settlement_flag
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Memory size83.2 KiB
False
42375 
True
 
160
(Missing)
 
1
ValueCountFrequency (%)
False42375
99.6%
True160
 
0.4%
(Missing)1
 
< 0.1%
2021-03-24T09:36:08.562778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

debt_settlement_flag_date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)39.4%
Missing42376
Missing (%)99.6%
Memory size332.4 KiB
Jun-2017
18 
Jan-2015
 
7
Apr-2017
 
7
Sep-2015
 
6
Oct-2013
 
5
Other values (58)
117 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1280
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)17.5%

Sample

1st rowFeb-2016
2nd rowMay-2014
3rd rowApr-2014
4th rowApr-2016
5th rowFeb-2015
ValueCountFrequency (%)
Jun-201718
 
< 0.1%
Jan-20157
 
< 0.1%
Apr-20177
 
< 0.1%
Sep-20156
 
< 0.1%
Oct-20135
 
< 0.1%
Jul-20145
 
< 0.1%
Feb-20155
 
< 0.1%
May-20145
 
< 0.1%
Nov-20134
 
< 0.1%
Apr-20154
 
< 0.1%
Other values (53)94
 
0.2%
(Missing)42376
99.6%
2021-03-24T09:36:08.723450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jun-201718
 
11.2%
apr-20177
 
4.4%
jan-20157
 
4.4%
sep-20156
 
3.8%
jul-20145
 
3.1%
oct-20135
 
3.1%
may-20145
 
3.1%
feb-20155
 
3.1%
apr-20144
 
2.5%
nov-20134
 
2.5%
Other values (53)94
58.8%

Most occurring characters

ValueCountFrequency (%)
2171
13.4%
1163
12.7%
0161
12.6%
-160
12.5%
J50
 
3.9%
u50
 
3.9%
n40
 
3.1%
e38
 
3.0%
p35
 
2.7%
433
 
2.6%
Other values (22)379
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number640
50.0%
Lowercase Letter320
25.0%
Uppercase Letter160
 
12.5%
Dash Punctuation160
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
u50
15.6%
n40
12.5%
e38
11.9%
p35
10.9%
r33
10.3%
a29
9.1%
c22
6.9%
b15
 
4.7%
t14
 
4.4%
l10
 
3.1%
Other values (4)34
10.6%
ValueCountFrequency (%)
2171
26.7%
1163
25.5%
0161
25.2%
433
 
5.2%
732
 
5.0%
531
 
4.8%
330
 
4.7%
613
 
2.0%
86
 
0.9%
ValueCountFrequency (%)
J50
31.2%
A28
17.5%
M21
13.1%
F15
 
9.4%
S15
 
9.4%
O14
 
8.8%
N9
 
5.6%
D8
 
5.0%
ValueCountFrequency (%)
-160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common800
62.5%
Latin480
37.5%

Most frequent character per script

ValueCountFrequency (%)
J50
 
10.4%
u50
 
10.4%
n40
 
8.3%
e38
 
7.9%
p35
 
7.3%
r33
 
6.9%
a29
 
6.0%
A28
 
5.8%
c22
 
4.6%
M21
 
4.4%
Other values (12)134
27.9%
ValueCountFrequency (%)
2171
21.4%
1163
20.4%
0161
20.1%
-160
20.0%
433
 
4.1%
732
 
4.0%
531
 
3.9%
330
 
3.8%
613
 
1.6%
86
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1280
100.0%

Most frequent character per block

ValueCountFrequency (%)
2171
13.4%
1163
12.7%
0161
12.6%
-160
12.5%
J50
 
3.9%
u50
 
3.9%
n40
 
3.1%
e38
 
3.0%
p35
 
2.7%
433
 
2.6%
Other values (22)379
29.6%

settlement_status
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)1.9%
Missing42376
Missing (%)99.6%
Memory size332.4 KiB
COMPLETE
142 
BROKEN
15 
ACTIVE
 
3

Length

Max length8
Median length8
Mean length7.775
Min length6

Characters and Unicode

Total characters1244
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOMPLETE
2nd rowCOMPLETE
3rd rowCOMPLETE
4th rowCOMPLETE
5th rowCOMPLETE
ValueCountFrequency (%)
COMPLETE142
 
0.3%
BROKEN15
 
< 0.1%
ACTIVE3
 
< 0.1%
(Missing)42376
99.6%
2021-03-24T09:36:08.886844image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-24T09:36:08.942429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
complete142
88.8%
broken15
 
9.4%
active3
 
1.9%

Most occurring characters

ValueCountFrequency (%)
E302
24.3%
O157
12.6%
C145
11.7%
T145
11.7%
M142
11.4%
P142
11.4%
L142
11.4%
B15
 
1.2%
R15
 
1.2%
K15
 
1.2%
Other values (4)24
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1244
100.0%

Most frequent character per category

ValueCountFrequency (%)
E302
24.3%
O157
12.6%
C145
11.7%
T145
11.7%
M142
11.4%
P142
11.4%
L142
11.4%
B15
 
1.2%
R15
 
1.2%
K15
 
1.2%
Other values (4)24
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin1244
100.0%

Most frequent character per script

ValueCountFrequency (%)
E302
24.3%
O157
12.6%
C145
11.7%
T145
11.7%
M142
11.4%
P142
11.4%
L142
11.4%
B15
 
1.2%
R15
 
1.2%
K15
 
1.2%
Other values (4)24
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1244
100.0%

Most frequent character per block

ValueCountFrequency (%)
E302
24.3%
O157
12.6%
C145
11.7%
T145
11.7%
M142
11.4%
P142
11.4%
L142
11.4%
B15
 
1.2%
R15
 
1.2%
K15
 
1.2%
Other values (4)24
 
1.9%

settlement_date
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct61
Distinct (%)38.1%
Missing42376
Missing (%)99.6%
Memory size332.4 KiB
Sep-2013
 
8
Oct-2013
 
8
Jan-2015
 
8
Jul-2014
 
6
Mar-2013
 
6
Other values (56)
124 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters1280
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)13.8%

Sample

1st rowFeb-2015
2nd rowNov-2013
3rd rowMar-2014
4th rowMay-2015
5th rowAug-2014
ValueCountFrequency (%)
Sep-20138
 
< 0.1%
Oct-20138
 
< 0.1%
Jan-20158
 
< 0.1%
Jul-20146
 
< 0.1%
Mar-20136
 
< 0.1%
Nov-20135
 
< 0.1%
Jul-20135
 
< 0.1%
May-20155
 
< 0.1%
Jun-20145
 
< 0.1%
Aug-20144
 
< 0.1%
Other values (51)100
 
0.2%
(Missing)42376
99.6%
2021-03-24T09:36:09.108736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sep-20138
 
5.0%
oct-20138
 
5.0%
jan-20158
 
5.0%
jul-20146
 
3.8%
mar-20136
 
3.8%
jul-20135
 
3.1%
may-20155
 
3.1%
nov-20135
 
3.1%
jun-20145
 
3.1%
jun-20154
 
2.5%
Other values (51)100
62.5%

Most occurring characters

ValueCountFrequency (%)
2184
14.4%
1164
12.8%
0162
12.7%
-160
12.5%
a47
 
3.7%
J47
 
3.7%
345
 
3.5%
u44
 
3.4%
437
 
2.9%
e34
 
2.7%
Other values (23)356
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number640
50.0%
Lowercase Letter320
25.0%
Uppercase Letter160
 
12.5%
Dash Punctuation160
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
a47
14.7%
u44
13.8%
e34
10.6%
n31
9.7%
r27
8.4%
p24
7.5%
c22
6.9%
y16
 
5.0%
t16
 
5.0%
l16
 
5.0%
Other values (4)43
13.4%
ValueCountFrequency (%)
2184
28.7%
1164
25.6%
0162
25.3%
345
 
7.0%
437
 
5.8%
526
 
4.1%
79
 
1.4%
67
 
1.1%
85
 
0.8%
91
 
0.2%
ValueCountFrequency (%)
J47
29.4%
M32
20.0%
A23
14.4%
O16
 
10.0%
F15
 
9.4%
S13
 
8.1%
N8
 
5.0%
D6
 
3.8%
ValueCountFrequency (%)
-160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common800
62.5%
Latin480
37.5%

Most frequent character per script

ValueCountFrequency (%)
a47
 
9.8%
J47
 
9.8%
u44
 
9.2%
e34
 
7.1%
M32
 
6.7%
n31
 
6.5%
r27
 
5.6%
p24
 
5.0%
A23
 
4.8%
c22
 
4.6%
Other values (12)149
31.0%
ValueCountFrequency (%)
2184
23.0%
1164
20.5%
0162
20.2%
-160
20.0%
345
 
5.6%
437
 
4.6%
526
 
3.2%
79
 
1.1%
67
 
0.9%
85
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1280
100.0%

Most frequent character per block

ValueCountFrequency (%)
2184
14.4%
1164
12.8%
0162
12.7%
-160
12.5%
a47
 
3.7%
J47
 
3.7%
345
 
3.5%
u44
 
3.4%
437
 
2.9%
e34
 
2.7%
Other values (23)356
27.8%

settlement_amount
Real number (ℝ≥0)

MISSING

Distinct143
Distinct (%)89.4%
Missing42376
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean4272.137875
Minimum193.29
Maximum14798.2
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:36:09.190187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum193.29
5-th percentile704.646
Q11842.75
median3499.35
Q35701.1
95-th percentile11040
Maximum14798.2
Range14604.91
Interquartile range (IQR)3858.35

Descriptive statistics

Standard deviation3119.373774
Coefficient of variation (CV)0.7301669246
Kurtosis1.155413328
Mean4272.137875
Median Absolute Deviation (MAD)1889
Skewness1.182311522
Sum683542.06
Variance9730492.741
MonotocityNot monotonic
2021-03-24T09:36:09.281173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18003
 
< 0.1%
80003
 
< 0.1%
65003
 
< 0.1%
50003
 
< 0.1%
16002
 
< 0.1%
21002
 
< 0.1%
40002
 
< 0.1%
120002
 
< 0.1%
12002
 
< 0.1%
33002
 
< 0.1%
Other values (133)136
 
0.3%
(Missing)42376
99.6%
ValueCountFrequency (%)
193.291
< 0.1%
295.81
< 0.1%
3001
< 0.1%
5001
< 0.1%
552.771
< 0.1%
ValueCountFrequency (%)
14798.21
< 0.1%
14161.281
< 0.1%
13066.151
< 0.1%
12426.221
< 0.1%
12390.991
< 0.1%

settlement_percentage
Real number (ℝ≥0)

MISSING

Distinct123
Distinct (%)76.9%
Missing42376
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean49.905875
Minimum10.69
Maximum92.74
Zeros0
Zeros (%)0.0%
Memory size332.4 KiB
2021-03-24T09:36:09.388476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10.69
5-th percentile28.489
Q140
median49.97
Q360.6525
95-th percentile76.4865
Maximum92.74
Range82.05
Interquartile range (IQR)20.6525

Descriptive statistics

Standard deviation15.5636903
Coefficient of variation (CV)0.3118608842
Kurtosis0.2054606155
Mean49.905875
Median Absolute Deviation (MAD)9.97
Skewness0.4461853083
Sum7984.94
Variance242.2284558
MonotocityNot monotonic
2021-03-24T09:36:09.477189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5013
 
< 0.1%
409
 
< 0.1%
654
 
< 0.1%
454
 
< 0.1%
49.983
 
< 0.1%
44.993
 
< 0.1%
203
 
< 0.1%
602
 
< 0.1%
692
 
< 0.1%
42.062
 
< 0.1%
Other values (113)115
 
0.3%
(Missing)42376
99.6%
ValueCountFrequency (%)
10.691
 
< 0.1%
19.461
 
< 0.1%
203
< 0.1%
22.041
 
< 0.1%
24.991
 
< 0.1%
ValueCountFrequency (%)
92.741
< 0.1%
91.71
< 0.1%
90.021
< 0.1%
87.251
< 0.1%
86.411
< 0.1%

settlement_term
Real number (ℝ≥0)

MISSING

Distinct11
Distinct (%)6.9%
Missing42376
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean1.2
Minimum0
Maximum24
Zeros126
Zeros (%)0.3%
Memory size332.4 KiB
2021-03-24T09:36:09.553169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.05
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.085254971
Coefficient of variation (CV)3.404379142
Kurtosis19.36316121
Mean1.2
Median Absolute Deviation (MAD)0
Skewness4.358151949
Sum192
Variance16.68930818
MonotocityNot monotonic
2021-03-24T09:36:09.623176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0126
 
0.3%
117
 
< 0.1%
25
 
< 0.1%
63
 
< 0.1%
242
 
< 0.1%
182
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
231
 
< 0.1%
111
 
< 0.1%
(Missing)42376
99.6%
ValueCountFrequency (%)
0126
0.3%
117
 
< 0.1%
25
 
< 0.1%
63
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
242
< 0.1%
231
< 0.1%
182
< 0.1%
121
< 0.1%
111
< 0.1%

Interactions

2021-03-24T09:34:33.919915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:34:34.012574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:34:34.105848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-03-24T09:35:42.254774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.323789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.394742image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.467888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.539947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.606383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.681078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.751265image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.822116image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.888072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-24T09:35:42.963542image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-24T09:36:09.852734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-24T09:36:11.923861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-24T09:36:13.979178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-24T09:36:15.939569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-24T09:36:16.276906image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-24T09:35:43.585108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-24T09:35:50.231404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-24T09:35:53.465386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-24T09:35:56.112861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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01077501NaN5000.05000.04975.036 months10.65%162.87BB2NaN10+ yearsRENT24000.0VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077501Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>credit_cardComputer860xxAZ27.650.0Jan-1985735.0739.01.00.0NaN3.00.013648.083.7%9.0f0.00.05863.1551875833.845000.00863.160.000.000.00Jan-2015171.62NaNAug-2018739.0735.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
11077430NaN2500.02500.02500.060 months15.27%59.83CC4Ryder< 1 yearRENT30000.0Source VerifiedDec-2011Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077430Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>carbike309xxGA1.000.0Apr-1999740.0744.05.00.0NaN3.00.01687.09.4%4.0f0.00.01014.5300001014.53456.46435.170.00122.901.11Apr-2013119.66NaNOct-2016499.00.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
21077175NaN2400.02400.02400.036 months15.96%84.33CC5NaN10+ yearsRENT12252.0Not VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1077175NaNsmall_businessreal estate business606xxIL8.720.0Nov-2001735.0739.02.00.0NaN2.00.02956.098.5%10.0f0.00.03005.6668443005.672400.00605.670.000.000.00Jun-2014649.91NaNJun-2017739.0735.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
31076863NaN10000.010000.010000.036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49200.0Source VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1076863Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>otherpersonel917xxCA20.000.0Feb-1996690.0694.01.035.0NaN10.00.05598.021%37.0f0.00.012231.89000012231.8910000.002214.9216.970.000.00Jan-2015357.48NaNApr-2016604.0600.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
41075358NaN3000.03000.03000.060 months12.69%67.79BB5University Medical Group1 yearRENT80000.0Source VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075358Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>otherPersonal972xxOR17.940.0Jan-1996695.0699.00.038.0NaN15.00.027783.053.9%38.0f0.00.04066.9081614066.913000.001066.910.000.000.00Jan-201767.30NaNApr-2018684.0680.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
51075269NaN5000.05000.05000.036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36000.0Source VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1075269NaNweddingMy wedding loan I promise to pay back852xxAZ11.200.0Nov-2004730.0734.03.00.0NaN9.00.07963.028.3%12.0f0.00.05632.2100005632.215000.00632.210.000.000.00Jan-2015161.03NaNFeb-2017564.0560.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
61069639NaN7000.07000.07000.060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47004.0Not VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1069639Borrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>debt_consolidationLoan280xxNC23.510.0Jul-2005690.0694.01.00.0NaN7.00.017726.085.6%11.0f0.00.010137.84000810137.847000.003137.840.000.000.00May-20161313.76NaNSep-2016654.0650.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
71072053NaN3000.03000.03000.036 months18.64%109.43EE1MKC Accounting9 yearsRENT48000.0Source VerifiedDec-2011Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1072053Borrower added on 12/16/11 > Downpayment for a car.<br>carCar Downpayment900xxCA5.350.0Jan-2007660.0664.02.00.0NaN4.00.08221.087.5%4.0f0.00.03939.1352943939.143000.00939.140.000.000.00Jan-2015111.34NaNDec-2014689.0685.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
81071795NaN5600.05600.05600.060 months21.28%152.39FF2NaN4 yearsOWN40000.0Source VerifiedDec-2011Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071795Borrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) <br>My score is not A+ because I own my home and have no mortgage.<br>small_businessExpand Business & Buy Debt Portfolio958xxCA5.550.0Apr-2004675.0679.02.00.0NaN11.00.05210.032.6%13.0f0.00.0647.500000647.50162.02294.940.00190.542.09Apr-2012152.39NaNOct-2016499.00.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
91071570NaN5375.05375.05350.060 months12.69%121.45BB5Starbucks< 1 yearRENT15000.0VerifiedDec-2011Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=1071570Borrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car.<br><br> Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.<br>otherBuilding my credit history.774xxTX18.080.0Sep-2004725.0729.00.00.0NaN2.00.09279.036.5%3.0f0.00.01484.5900001477.70673.48533.420.00277.692.52Nov-2012121.45NaNDec-2016504.0500.00.0NaN1.0IndividualNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN

Last rows

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4252681085NaN10500.010500.0275.036 months11.22%344.87CC4Town of Plainville3 yearsRENT60000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=81085Replace my existing roof.otherroof061xxCT19.50NaNNaN740.0744.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.011219.852169293.8610500.00719.850.00.000.00Aug-20080.00Aug-2008Jun-2007744.0740.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4252777792NaN3000.03000.0125.036 months9.01%95.42BB2Tanks Tavern< 1 yearRENT35000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=77792To pay off credit card and rent for the summer since I graduate at the end of the July.otherDep4774665xxKS10.00NaNNaN715.0719.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.03434.999452143.123000.00435.000.00.000.00Jul-201095.66Jul-2010Jun-2010594.0590.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4252877757NaN3000.03000.00.036 months9.33%95.86BB3NaN< 1 yearOWN20000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=77757For home improvementotherHome improvement024xxMA10.00NaNNaN710.0714.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.03450.9105260.003000.00450.910.00.000.00Jun-201096.77Jul-2010May-2007714.0710.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4252974505NaN2000.02000.0225.036 months9.96%64.50BB5NaN< 1 yearRENT6000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=74505I just need enough money to make it until the fall.otherSummer stuff325xxFL10.00NaNNaN685.0689.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.02322.408993261.272000.00322.410.00.000.00Jul-20100.84Aug-2010Jul-2010594.0590.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4253074323NaN6500.06500.00.036 months9.64%208.66BB4Air Force< 1 yearRENT20000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Charged Offnhttps://lendingclub.com/browse/loanDetail.action?loan_id=74323I'm in the military and before I came in I racked up a large credit card debt. With all the different payments to keep track of and extra charges it is really hard to get out from under them. With just one payment a month it would make my life easier, so I can concentrate on my job.otherOne-Debt Loan064xxCT10.00NaNNaN710.0714.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.07193.0600000.001791.32503.810.04897.931714.27May-2008208.65Dec-2008Aug-2018534.0530.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4253173582NaN3500.03500.0225.036 months10.28%113.39CC1NaN< 1 yearRENT180000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=73582I am getting married on July 28 and will need another $3,500 to pay the suppliers (catering, hotel, etc.). I would have never thought getting married was such an expensive proposition! Thanks for helping.otherWedding coming up100xxNY10.00NaNNaN685.0689.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.03719.431070239.113500.00219.430.00.000.00Mar-20080.00Mar-2008Feb-2013819.0815.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4253272998NaN1000.01000.00.036 months9.64%32.11BB4Halping hands company inc.< 1 yearRENT12000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=72998I would like to buy some new furniture in my apartment and TVotherdelight021xxMA10.00NaNNaN695.0699.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.01155.6008990.001000.00155.600.00.000.00Jun-201032.41Jul-2010Sep-2014784.0780.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4253372176NaN2525.02525.0225.036 months9.33%80.69BB3NaN< 1 yearRENT110000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=72176I need to pay $2,100 for fixing my Volvo :) Any help appreciated!otherCar repair bill100xxNY10.00NaNNaN710.0714.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.02904.498829258.822525.00379.500.00.000.00Jun-201082.03Jul-2010May-2007714.0710.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4253471623NaN6500.06500.00.036 months8.38%204.84AA5NaN< 1 yearNONENaNNot VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=71623Hi, I'm buying a used car. Anybody on facebook wants to finance me? ThanksotherBuying a car100xxNY4.00NaNNaN740.0744.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.07373.9049620.006500.00873.900.00.000.00Jun-2010205.32Jul-2010Aug-2007724.0720.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN
4253570686NaN5000.05000.00.036 months7.75%156.11AA3Homemaker10+ yearsMORTGAGE70000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paidnhttps://lendingclub.com/browse/loanDetail.action?loan_id=70686I need to make several improvements around the house - fix garage, fix back fencing, and misc other.otherAroundthehouse068xxCT8.81NaNNaN770.0774.0NaN0.0NaNNaNNaN0.0NaNNaNf0.00.05619.7620900.005000.00619.760.00.000.00Jun-2010156.39Jul-2010Feb-2015794.0790.0NaNNaN1.0IndividualNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNCashNNaNNaNNaNNaNNaNNaN